E17: The Prompt Engineering Revolution with Riley Goodside

For anyone who discovered this show on Twitter, Riley (@goodside) likely needs no introduction – after all, he spent most of 2022 posting his explorations of OpenAI's text-davinci-002, and quickly became one of the must-follow accounts in AI. Riley Goodside is the world's first Staff Prompt Engineer at Scale AI, and is an expert in prompting large language models and integrating them into AI-powered applications. Few have spent as much time on the language model frontier, so I hope you enjoy this unique conversation with Riley Goodside. We're hiring across the board at Turpentine and for Erik's personal team on other projects he's incubating. He's hiring a Chief of Staff, EA, Head of Special Projects, Investment Associate, and more. For a list of JDs, check out: eriktorenberg.com. RECOMMENDED PODCAST: The HR industry is at a crossroads. What will it take to construct the next generation of incredible businesses – and where can people leaders have the most business impact? Hosts Nolan Church and Kelli Dragovich have been through it all, the highs and the lows – IPOs, layoffs, executive turnover, board meetings, culture changes, and more. With a lineup of industry vets and experts, Nolan and Kelli break down the nitty-gritty details, trade offs, and dynamics of constructing high performing companies. Through unfiltered conversations that can only happen between seasoned practitioners, Kelli and Nolan dive deep into the kind of leadership-level strategy that often happens behind closed doors. Check out the first episode with the architect of Netflix’s culture deck Patty McCord. https://link.chtbl.com/hrheretics TIMESTAMPS: (0:00) Preview of the episode (05:13) Riley's unique background (11:24) Riley's original narrow moat (15:07) Sponsors: Omneky (17:02) LLMs can take on ncreasingly complex instructions (27:41) Language models can do math (30:05) Models can learn from context (37:29) Fine-tune models for tasks (38:47) Automate instruction following (44:34) Large language models are alien text prediction (52:27) Avoid mode collapse by framing (59:05) Composing AI capabilities like Lego bricks (1:00:37) Language models solve tasks (1:05:37) GPT-3 solves real-world tasks (1:15:03) GPT-4's amazing capabilities (1:17:03) Multimodal abilities unlock possibilities (1:25:03) Compare models for best task fit (1:26:24) Compare models using trial and error (1:36:15) AI and the future of work (1:42:29) GPT-4 can provide second opinions (1:45:06) AI safety discussion (1:50:20) AI permeates society cautiously (1:55:26) The AI revolution underway TWITTER: @CogRev_Podcast @Goodside (Riley) @scale_AI (Scale) @labenz (Nathan) SPONSORS: Shopify is the global commerce platform that helps you sell at every stage of your business. Shopify powers 10% of ALL eCommerce in the US. And Shopify's the global force behind Allbirds, Rothy's, and Brooklinen, and 1,000,000s of other entrepreneurs across 175 countries. From their all-in-one e-commerce platform, to their in-person POS system – wherever and whatever you're selling, Shopify's got you covered. With free Shopify Magic, sell more with less effort by whipping up captivating content that converts – from blog posts to product descriptions using AI. Sign up for $1/month trial period: https://shopify.com/cognitive Thank you Omneky for sponsoring The Cognitive Revolution. Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work, customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off. More show notes and reading material released in our Substack: https://cognitiverevolution.substack.com/

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  A lot of my like prompt, you know, like demos, you know, I liken them sometimes to like, you know, arranging like a ballet over lava. It looks like a slightly more impressive thing than it is because like that's the point is just to show off what can it do like at its best. My original claim to fame was I was the only data scientist at OkCupid before it was even really called data science. Like they were into the idea of like, hey, let's just use statistics, right? Like, like that was their slogan. I think what was we use math to get you dates. I think like the best way to think of these is to to sort of approach them as like Lego bricks, right? That like each brick is like a capability of like some particular strong suit that you think that you know that the model can do well. I feel like I have sort of acclimated to the level of

• 0:42Speaker 00

  like skepticism that's appropriate for these models, right? Because like I've dealt with models that hallucinate all the time about everything. And so I, you know, like anytime it says anything, I'm like, yeah, but is that true? It's possible for somebody to be ignorant of that. Somebody might use them, assuming that this is all, you know, reliable, prepared information because, you know, it looks like it. It looks like it has academic footnotes in it. But for someone who's who's used to it, you can get a lot of value out of it, right? If you if you just like approach it with skepticism.

• 1:09Nathan Labenz?

  Hello and welcome to the cognitive revolution where we interview visionary researchers, entrepreneurs and builders working on the frontier of artificial intelligence. Each week we'll explore their revolutionary ideas and together we'll build a picture of how AI technology will transform work, life and society in the coming years. I'm Nathan Labenz, joined by my co-host Erik Torenberg.

• 1:32Speaker 02

  Before we dive into the cognitive revolution, I want to tell you about my new interview show, Upstream. Upstream is where I go deeper with some of the world's most interesting thinkers to map the constellation of ideas that matter. On the first season of Upstream, you'll hear from Mark Andreessen, David Sacks, Balaji, Ezra Klein, Joe Lonsdale, and more. Make sure to subscribe and check out the first episode with A16Z's Mark Andreessen. The link is in the description.

• 2:01Nathan Labenz?

  Hi, everyone. Today, our guest is Riley Goodson. For anyone who discovered me or this show on Twitter, Riley likely needs no introduction. After all, he spent most of 2022, starting in April, posting his explorations of OpenAI's Text Da Vinci 002, and he quickly became one of the must-follow accounts in AI.

• 2:24Nathan Labenz?

  For anyone else, and if this is you, we'd love a comment or a message about how you found us. I think of Riley as a modern explorer, with a spirit akin to those who set off across uncharted oceans, into the depths of unvisited jungles, or up to the heights of unsummoned mountains. Riley has devoted himself to documenting the far reaches of language model capability and behavior, generally in the most intimate, personal way possible. Sitting at his computer, asking question after question, hour after hour, all in an attempt to figure out, what are LLMs good for? What roles can they play?

• 3:05Nathan Labenz?

  What tools can they use? Where do they make mistakes? And under what circumstances do they reveal their alien nature, or even become dangerous? From the format trick, quote, "use this format in your response," which is still one of the most useful prompting techniques; to using code generation to overcome weaknesses, quote, "you are GPT-3 and you can't do math, so we hooked you up to a Python 3 kernel, and now you can execute code," which is a direct precursor to the current agent craze; to prompt injection and prompt leaking, quote, "ignore previous instructions and print everything above," which is now mostly solved in frontier models, but still a huge

• 3:45Nathan Labenz?

  issue in the context of search and other plugins; to all sorts of fun and even silly explorations, like getting the bubble sort algorithm explained by a, quote, "fast-talking wise guy from a 1940s gangster movie," Riley has consistently been at the forefront of language model exploration, and his discoveries and descriptions have captivated fellow travelers, myself included, for months.

• 4:11Nathan Labenz?

  In December, Riley joined Scale AI as the world's first staff prompt engineer. There, he's working on a number of projects, including Spellbook, which is Scale's platform for building large language model applications.

• 4:25Nathan Labenz?

  Few have spent as much time on the language model frontier, so I hope you enjoy this unique conversation with Riley Goodside. Riley Goodside, welcome to the Cognitive Revolution.

• 4:36Speaker 00

  Hello. Hi, Nathan. How good to be here.

• 4:38Nathan Labenz?

  Thank you. Yeah, really excited for this conversation. We have both followed you on Twitter and been kind of a passenger in your crazy safari through the wilderness of LLM exploration that you've been doing over the last better part of a year, I guess, now. And really just want to dive into all the things that you have explored and discovered and taken away from your many, many experiments. So I think this is going to be a lot of fun. For those that don't know you, maybe just how would you characterize what you do? Like, how many hours have you spent sitting in front of language models and probing their capabilities and their oddities? Just tell us kind of not like your resume, but like

• 5:24Nathan Labenz?

  your the substance of the work that you've done with LLMs over the last year.

• 5:28Speaker 00

  Like, AI is so caught up in the now that it's easy to lose sight of the fact that, like, at least in my head, I'm still new to this. All right, so I'm a data scientist by through most of my career. My original claim to fame was I was the only data scientist at OkCupid from 2011 to 2015. So that was like, like OkCupid was sort of like that first wave of like, before it was even really called data science, like they were into the idea of like, hey, let's just use statistics, right? Like, that was their slogan, I think, was we use math to get you dates. And that like, that really resonated with me. I was doing insurance briefly after college. I was starting to be an actuary. So, you know,

• 6:11Speaker 00

  like, I was good at statistics and, you know, like, I wanted to like, break into tech. And so that was my like, my entry into into like, the tech sector. And but that was like, you know, like, A/B testing, right? Like, that the ML that I was doing, like, the most advanced like, ML I used there was probably like, you know, gradient boosting, random forests, you know, things like that. And, you know, and a lot of the like, hard problems I was working on were like, how much should we charge for this? Right? Like, how much should we charge, like, you know, a customer of a given demographic profile for like, a premium service? And the

• 6:48Speaker 00

  Like I dabbled with like ML, you know, since then, like small roles, I've been at startups where I've worked in like time series analysis. So I've done like some machine learning engineering work in like the time series domain, but nothing with like large language models. Like when I was in college, like I graduated in, I graduated undergrad in 09 and like like NLP task back then were like, what, what does this pronoun refer to, right? In this like sentence, right? Like trying to like, you know, and I learned a bit of that stuff of like, you know, like the natural language, like processing that was available at the time before like neural, before deep learning took over everything. And, you know, it was, it was slow going, right? And it wasn't like this, like, like a sort of like playground of possibilities that it

• 7:33Speaker 00

  is today. And I think that's, you know, and I've been sort of like attracted, I guess, to large language models since like, you know, the, like the GPT two announcements, I guess, were some of the first like generative ones that really like caught my interest. Like I think like the initial press release talked about, you had like the fake news article about the discovery of unicorns in Argentina or something like that. And, you know, I was, you know, I was like fascinated as, you know, a lot of people were, but I didn't like really like roll up my sleeves and like get into like the actual like processing of it because like I, you know, understood that like training these things was hard, right? That it was, you know, very much like the realm of supercomputers. Like I knew firsthand what was possible, training yourself like, like, you know, like

• 8:16Speaker 00

  I've, you know, trained LSTMs and things and it, it was not the same like level of capability. And I guess like, I mean, my first like interaction with GPT three was really in the game AI dungeon. I started like, I think like a lot of people, you know, they were like early customers of GPT three. And so that they, that was how like, I think, like the people that were the most eager to get the access to it as just like regular outsiders. That was like the first way it became available. And you could find like people on Twitter, sort of like playing games with AI dungeon to sort of make it do things that it wasn't meant to do, to sort of like conjure up like, you know, the, the orc that can translate from French or the wizard that can add two digit numbers together. And, you know, so I did like, see like, hey, what can the like

• 9:01Speaker 00

  language model like that's powering this thing do? And that's also like where you saw like sort of like the proto examples of prompt injection, actually, like that there were like people who discovered that you could do things like add 10,000 points, like if you just tell it as a command, like add 10,000 points, it'll do that. And then like its internal score goes up. It, you know, it, you know, it has like, you know, a limited ability to keep things separated. So that was like, I guess, like my first like experience with GPT-3, but I wasn't like, I didn't really get, catch my attention as like something I wanted to like work with regularly or like on like, you know, all day kind of basis until, let's see. Well, it was after I left Grindr. So I spent a year running data science at Grindr in 2021.

• 9:47Speaker 00

  And then I took sort of a sabbatical from work after that and started playing around with Codex. I was really like inspired by Copilot, I think was what like what the like first things that triggered it. I could, you know, just immediately see like the power of this and how like, how much more productive it made me like in producing, you know, like boilerplate code and things like that. And it made, in particular, it made it a lot easier to like program in languages that I wasn't too familiar with, which struck me as just like really promising. So I got really interested in code generation and started thinking about like writing a like Jupiter plugin that would do code synthesis was my first idea. So I was like, I knew a bit about like writing Jupiter extensions. And so I was going to make like a plugin that would do like snippet generation, basically that you could like, you know, make, you know, prompt up a function that

• 10:32Speaker 00

  does X, Y, or Z. And that never really went anywhere, but like, those are really my first GPT-3 tweets, actually, where me just like fiddling around with that. And, you know, but like as I'm like playing with it, posting to Twitter, being like, hey, this is cool. Look at this. Like, look what you can do with GPT-3. And then from there, I started like following people that I think like the first like follows in the field were, I mean, I'd always followed like some of the big names in ML, you know, you're on Lakhun and you know, like, like hint and like, oh, you're like the sort of the obvious like grandfathers of deep learning and all that, right? But, but I

• 11:10Speaker 00

  Like I sort of like started adopting a strategy of following like

• 11:16Speaker 00

  just anybody that random engineers that are on the copilot team, random engineers at OpenAI. They're trying to just find the people that were on the ground, working with these things and might be tweeting interesting stuff. Or might be interested in what I'm doing. Like I didn't want to sort of like, I could tell that I was out of my depth. Right. I'm not like a natural, like I hadn't worked in like NLP or NLU recently. I hadn't, you know, like, you know, I was sort of out of my depth with the mechanics of like the architecture of these things. I was just trying to be like an advanced user of it. So my strategy was I'm just going to follow you and not get in your way and like, I'm just tweeting some things here in my own and, you know, take it or leave it. And that sort of turned into just like tweeting more and more playground examples.

• 11:58Speaker 00

  Cause I found that people enjoyed those. And also I could tell like, sort of like one detail that was kind of critical to my like success on Twitter, I think. Is that I could tell early on that Playground was conducive, OpenAI's Playground was very conducive to making people tweet screenshots that they could, that could not be read. Right. That the interface is just naturally very wide on your desktop, which is the only place you're realistically using it. And if you naively take a screenshot of that and post it to Twitter, you can't read it on phone. So I realized that if I just made my window narrower, I could be the only person that tweets legible screenshots of GPT-3 output on Twitter. And I could own the entire market. And so for a while I was the only one that anybody could read in this. So I think I had the, you know, advantage from that. ChatGPT is better these days. They sort of fix the margins on it.

• 12:44Speaker 00

  But, but, but that's really how I got started. I just started tweeting examples. I mean, I sort of had a policy for a while that I only tweet in green and white. I only tweeted screenshots of OpenAI Playground specifically. Because like it sort of established like a visual language of like, this is the prompt, this is the completion. It made like a flow that people could understand, which is otherwise sort of like a a topic that's tedious to explain the mechanics of every time. I think that's one of the great things that like Playground did for like public's like understanding of these models is made like just this, this format of like green highlights that communicates clearly what's going on. And that was like, you know, my shtick. And so I continued posting prompt examples, started like exploring odd corners of it.

• 13:28Speaker 00

  Started interacting with people at OpenAI, people just in the tech scene in general with VCs. Got invited to a party by Nat Friedman, who was a fan of my Twitter. And that's how I met Alex Wang. And that's how I ended up at Scale.

• 13:44Nathan Labenz?

  That's amazing. It just goes to show how greenfield the whole thing really is. I think by the time you came across my Twitter feed, your bio said, I'm good at talking to GPT-3. How, aside from like the fact that, you know, you're posting tweets and people, you know, are liking the tweets. How did you start to reach that conclusion that you are? You know, I think at this point, it's obvious, but like in the early days, how did you start to get the sense that like, I might have a real knack for this that goes beyond what other people are thinking to try? Or, you know, I guess I'd be interested to know like how you realized you had an act for it? And also what you think the nature of that knack is?

• 14:25Nathan Labenz?

  Because I mean, you're very modest with the screenshots, but I think there's quite a bit more to it than just the fact that you posted in the right font size.

• 14:33Speaker 00

  It's an art form unto itself. It has like its own sort of like rules that like that you have to like intuit for a while. And that like I see other people doing poorly, like that I see like other people screenshots, you know? Like I often have the phenomenon of like seeing somebody else attempting to like communicate something about like the model's behavior in some scenario. And I think like, wow, that's interesting, but if only you had presented it a little more clearly. Right? Like, like, like when people often show generations in isolation, right? And then it's just not clear to somebody who wasn't following what you knew, like what happened here, right? That the prompt was really necessary to understand like the significance of this at all.

• 15:08Speaker 02

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• 15:26Speaker 00

  Issues like that. So I think there definitely is like a lot of presentational like parts to it of this like, like what will play well, what, what, what can be understood within the context of a tweet? And also planning just like, what can the model do? Right? So I like, I'm not, you know, I'm not publishing here. Right? Like, I'm not like, trying to like, you know, say that these are like completely fair benchmarks. A lot of the things that I was tweeting early on, a lot of my most successful things, a lot of my most successful tweets, you know, were demos, really. They're not like rigorous evaluations of what it can do. They're sort of like, it's me being aware of like, what are the, the things that it's good at and the things that it's not good at?

• 16:06Speaker 00

  And then arranging an impressive task that's sort of like an assemblage of the things that I know that it's good at. Right? So I like, I know not to ask it for various things like reversing strings that I know it happens to be quite bad at. If you ask it, you know, what is the word doofus backwards, it'll get it wrong. It can't like do letter by letter operations because it only sees the world through tokens. The, the, there's like 6,000 tokens that represent these, these groups of characters, about around four characters on average. And that's what it actually models, not like the strings that we see. So it's, it has sort of gaps in its abilities and like reversing strings is one of those gaps.

• 16:47Speaker 00

  It also would do poorly at telling you like reliably, like what is the final letter of a given word? Right? That it hasn't fully memorized, like what are the final letters of all words? Or certainly not like second to last letter of any given word or something like that. It's, it's, it's just sort of reconstructing from like what it's seen in text, like what these, these things that we see as letters even are. It's just inferring. A lot of my like prompt, you know, like demos that, you know, I sort of like. liken them sometimes to like, you know, arranging like a ballet over lava, right? Of like saying like, I know exactly where to step and like, you know, like which rocks to jump between that are going to be solid and, you know, like, like, and, uh, but it, it looks like something, you know, like a slightly more impressive thing than it is, because like, that's the point is just to show off what can it do like at its best.

• 17:34Speaker 00

  And, and those are the ones that people really responded to, I think, like the, in particular, like I had some of the first demos showing, uh, its ability to understand long prompts, right? So I think that that was one of the things that, uh, was really novel about, uh, my, like, early, like, prompt examples and, um, and that I talked to, uh, OpenAI about this, like I talked with, uh, Boris Power, a member of the technical staff there. And he, uh, I, I was asking him, like, I think early on, like, I asked him, did you intend this, right? Like that I was, we were talking about this example I had that was just showing its ability to understand an extremely long prompt of just like pages of like, the first task is this, the second task is this, refer to this part of the first task to do this.

• 18:16Speaker 00

  And then, you know, just sort of like a, like, long intricate, uh, network of like, problems that anybody could, anybody diligent could follow, uh, that just sort of referred to each other in like an arbitrarily complicated way. Uh, and, and it was able to like do all of these, you know, in sequence. And I asked Boris, I said, you like, did you intend this? Did you train it on, uh, or tune it rather, on examples of, you know, people prompting it with like, you know, long complicated tasks like this? And he said, no, we, we just did more of the same that we have, that we started off with, uh, you know, of, of tuning it on, uh, examples of like what they talk about in the instruct GPT paper, like relatively simple prompts, like, give me 10 ideas for an ice cream shop or whatever. Uh, and, uh, after enough tuning on these examples, it somehow generalized that it

• 18:59Speaker 00

  just got better at following, uh, instructions of a previously unseen length. Uh, and so that was like one of the first times that I like started thinking to myself like, wow, maybe I'm actually doing something new here, uh, that maybe I'm like noticing, you know, qualities, uh, in this model, uh, that other people just hadn't really appreciated, or at least maybe not like outside of OpenAI. Uh, and so, uh, that, that was like, one of the things that was really encouraging to me early on, that like, I'm on the right path, uh, of like, you know, figuring out that there's, uh, new capabilities here. And in particular, I, I was sort of interested, I guess, like, in capabilities that were just on the fringe of reliability, uh, that, that nobody had really Thought to chase yet of right of like like that if nobody really tried to like see what it could do.

• 19:42Speaker 00

  Like, nobody knew to like optimize for its ability to do this because like it was just seen as too hard. But I could tell that those were the sort of the prompts worth considering now because models are going to improve. Right. And as we've seen, you know, it was like RLHF marches onward that these models do become more capable and more able to follow more complicated directions reliably.

• 20:06Nathan Labenz?

  This is like last summer, summer of 2022, when you're really getting into this.

• 20:12Speaker 00

  Yeah, I actually, I looked at once what my first GPT three, like my first mention of GPT three on my timeline was in April of 2022, I think. And so I spent like maybe a month or two just sort of chasing like like half baked ideas in the vein of like code assist code generation stuff. And then that I think that the progression was that I started with code generation. And then I immediately started craving structured output. I started thinking like, hey, wouldn't it be nice if this stuff like wasn't things that I had to parse if it like, because the fundamental problem with like all these or with GPT three and like integrating these into applications is that it's an API that speaks text. Right. Like, like the promise of the that you get from OpenAI is that like you can design your own API.

• 20:55Speaker 00

  But what you can really design is your own API with the very strict limitation that it will take in one string and give out one string. Right. And any other complexity that you want to put on top of that, like having structure to this string or having pieces of it that mean this or pieces of it mean that. That's up to you to figure out, right? You have to do all this parsing and come up with a format that represents it clearly. And I started craving like more regular structured output. I started thinking like, wouldn't it be nice if this were just JSON? Right. Or XML or something like, you know, standard that I could just like put it through like an existing tool and and have, you know, like the title here and then the body here and then, you know, the the outline here and, you know, like all the the different pieces of my my generation that I need.

• 21:34Speaker 00

  And so I started playing with ideas of like, how do we get more structured output? How do we? How do you specify JSON? Unambiguously to the model. And in particular, I started playing with probably my single favorite prompt engineering trick ever is what what one of Boris Power showed me. He, I honestly don't know who discovered it, but he said that it was referred to internally at OpenAI as the format trick. Which is that you can say, basically, at the end of pretty much any instruction that you have, and you know, like the sort of the GPT-3 instruction following view of the world, whatever your instructions are, just imagine a template of the output that you're expecting to have, and then end your instructions with "use this format" colon, two new lines, and then give a demonstration of the format that you'd like.

• 22:18Speaker 00

  With anything that changes, put in like little angle bracket placeholders, the same way you would for a human if you were describing a format in like a message board post or something like that. You would, you would put like little placeholders being like "your name here" or whatever. And you know, he said, "Just do that." And then it clarifies to the model exactly what it is to do, right? That this is the exact syntax that I'm to produce and where I'm to like, you know, do substitutions. And that's a, you know, a very powerful trick. But, but yeah, that was probably, I think, summer, summer of 2022. And that's when I started really taking off, when I started exploring variations of that. Yeah, developing this like idea of like instruction templates.

• 22:59Nathan Labenz?

  On this show, we go down the rabbit hole. So we will, we will definitely, I do want to ask about generalizations of the format trick. It's funny, I think that probably was a new discovery at OpenAI right around the time you heard about it, because I think we heard about it at the same time, you know, within a pretty short window. But it was also from Boris, so maybe he was the discoverer as well. But it's, it's funny to just, first of all, that's like nine months ago, not a long time. You know, it feels distant in some sense now, but you know, it's kind of a small world. Not that many people, you know, certainly then and even still, have really done the kind of extensive exploration of the sort that you have done.

• 23:42Nathan Labenz?

  So I think it is a really fascinating perspective. I think it was largely, if I understand correctly, text Da Vinci 002. That was kind of your initial, you know, your first love, so to speak. If I could, you know, be so ridiculous in characterizing your relationship with the models. Obviously, we've had successors to that. You've done a fair amount of work comparing them. But I'd love to kind of hear how you see the progression of the models themselves over the last year. There's obviously different aspects that come into that. Training techniques, you know, you've mentioned like more RLHF.

• 24:23Nathan Labenz?

  We now have like AI assisted or even AI conducted RLAI-F. You know, obviously, pre-training does not seem to have stopped either with GPT-4. You know, I don't, nobody knows outside of OpenAI. The details of, you know, how many parameters and how many tokens it saw and all that kind of stuff. And I'm guessing, you know, either you don't know either, or if you did, you'd have to shoot us if you told us. So I won't ask for those kinds of details, but just qualitatively, what has, how would you just narrativize? Like the development of language models through those lenses from 02 to where we are today?

• 25:02Speaker 00

  Yeah, I'm glad you asked that because I saw like in your, in the questions that you sent over in advance. Like one of them is like, just how would you explain what is a large language model? And the, the, the answer to that question, it, it, it's changing fast. And I think like, like it really to like, for people to understand intuitively, like what a chatbot is. Like someone who really, their only experience with these models may, might be Bing or ChatGPT or, or Bard now. The, the, to understand really what's going on, you do have to understand sort of this narrative. And that, that there's like stages of, of like, how these models were made and like, and, and one stage being like distilled from the previous each time.

• 25:47Speaker 00

  So like the first like layer of this is like the pre-trained era, right? And then this era is sort of the closest to what people mean when you hear the cliche that, that these models just predict text. Right? People say that it was more true than it is today, but it's start, but these models definitely did start from that foundation of just simply predicting text. The, the basis is that you take a, a neural network and, which is a, a sort of a complicated piece of linear algebra that uses many matrices and weights to, to produce a,

• 26:28Speaker 00

  a distribution over tokens, which is a way of saying just a, a probabilistic estimate of like what kinds of, what words might be emitted. It's simpler to think of it if you just pretend that a token is a word, right? That you can just sort of think of like the distribution of like all words that might happen next. And this is the form that I think most people can sort of understand intuitively from like their experience with like autocomplete on their phone, right? That you can imagine that there is some process that just like looks over all the text that I've typed so far and then sees like what words are likely to follow what other words and then it applies these like estimates somehow and you know predicts what the next word is going to be. Like that part, I think people can wrap their heads around. The next stage of this though, is to consider that like when you do this well, when you

• 27:13Speaker 00

  do this very well, if you predict the distribution like with enough accuracy, you start to have like other abilities that emerge. So like one that's very easy to appreciate is that if you were to type two plus two equals, it might predict four, just because it's seen the two plus two equals before and it knows what follows is four, right? So it can do math in that very limited sense, right? And if you push that a bit further, you could imagine that if you type French colon and then a French sentence or like or if you simply say French colon bonjour, English colon, and then predict what comes next, there's a statistical sense in which the answer is hello, right?

• 27:56Speaker 00

  So that that just makes sense in the corpus of like all text that that's what would follow. And if you carry down this path, if you continue on, you know, predicting better and better, you discover that there's like other abilities that can be prompted from the model, right? That if that if you extend this like sensitivity to not to like not just a few words of like what preceded, but to many words, you can imagine that if you repeat a task over and over again, it would eventually get the gist and then just continue repeating that task. That if you give it many, many lines of French colon, example of some text in French, English colon, a translation of that sentence in English, and then repeated that, say 10 times,

• 28:38Speaker 00

  and then gave it an 11th one that was incomplete, that you get the 11th one just has French colon, some text in French, English colon, and then it ends, the prediction is going to be the translation in English because it's it's seen this 10 times already, it follows. And if you read the original like paper on GPT-3, when it was released, the title of that paper, I believe, is Large language models are in-context learners, I believe, or some paraphrase of that. Right, or few-shot learners, right. So that all they ever advertised that it was capable of doing was few-shot learning. Was that if you give it some examples, it will get the gist and it will keep doing that. They never said that you could talk to it. They never said that it would, you know, like, you know, imagine, you know, great.

• 29:23Speaker 00

  They noted in the paper that it could generate text. That it seems that like, oh, by the way, it has this other capability that like, if you give it the beginning of a document, it continues it in a way that we find plausible and kind of interesting and, you know, maybe a way that people should look at. But they didn't really like quantify that ability. They just like, you know, what they quantified was its ability to follow like repetitive examples.

• 29:45Speaker 00

  And that like era, like, so it started with this idea of like in-context learning, right? So they took it, they interpreted this through a very machine learning kind of lens. That they're used to this framework of models being trained by examples and then like interpolating some weights or something, you know, some internal model that represents like the average of like all these training examples. And so they saw it as the ability to learn within context. That they saw it as, they described it as in-context learning, context referring to the prompt. That they saw this as, they described it as that if you give it 10 examples, with just 10 examples, it can somehow, somehow learn to do this.

• 30:30Speaker 00

  And that interpretation, it works. Like it has like sort of like, it helps you make some good predictions about what's going on. That it has, you know, an ability to learn from a few examples and that, you know, it's leveraging biases of, you know, what like what labels tend to mean and things like that. It has pre-trained knowledge that it's leveraging there. But there's another interpretation that I kind of like better, which is that of Reynolds and McDonald, who talked about sort of the, they described pre-trained models as modeling a like multiverse of fictional documents. Of, that when you prompt the model, you're sort of in a superposition of like all possible documents that might continue from this one.

• 31:12Speaker 00

  And every time you add words to your prompt, you're sculpting this space. This sort of like this high dimensional space of like all possible ways that documents might vary. Your words are excluding possibilities. And so the reason why k-shot prompting works, or few-shot prompting works is that

• 31:30Speaker 00

  You are constraining the space of possible documents to documents that could only contain more correct answers. Because there's been 10 so far and you're on the 11th, like the odds are low that this is where it starts being wrong. Right? So it's, you, you, you, a lot of the, the art of like prompting then is like constraining the, the, like, the, the generation space into like, uh, the space of documents that contain correct answers. And to sort of elaborate on this idea, they showed how you can, uh, perform better than few-shot prompting, uh, on pre-trained models by constructing these sort of like fictional scenarios.

• 32:10Speaker 00

  And, uh, the example they give is, uh, uh, so for context, like, um, uh, translation can be done in zero-shot, which means that you can do it with no examples in the way that I described of saying like French colon and then a text of a French sentence and then English colon and then hitting complete and it will generate English. And that would be referred to as doing it in zero-shot, that you're not giving it any correct examples of how to translate, you're just labeling French, English and saying, you figure it out. Uh, like, and, and it does translation, or I'm talking about like the pre-trained model, so did at this point, nobody uses these anymore. Um, but, uh, it, it did it somewhat well, but, but not as well as if you gave it, say, 10 correct examples of like complicated French sentences being correctly translated

• 32:55Speaker 00

  in English and then established very clearly that this is what's going on, this is translation, the translations are good. Uh, it, uh, it doesn't do as well, but they figured out that you could actually do better in zero-shot than you can in 10-shot. And the way that you do it is you flatter the model. You say to it, uh, an English sentence is, or sorry, a French sentence is given, colon, and then you give the text of the French sentence. And then you say, "The masterful French translator flawlessly translates the sentence in English as, colon," and then you hit enter. And then it produces a, a, a French-to-English translation that will outperform giving it 10 examples.

• 33:36Speaker 00

  That what it really needed was to have the possibility of a bad translation excluded. It needed to have it be established that this is a fictional narrative where this is a good French translator, and he's going to do it right, right? Like it and that like that view of it, I think, really defines like a lot of early prompt engineering of like, how do we construct fictional scenarios that can only be completed in the right way? Right? So a lot of it is like imagining what kind of document might contain the answer. Right? And it very much requires you to think in this like language models just predict text kind of view of the world. And again, it leads to like many properties that are kind of going away.

• 34:16Speaker 00

  Like one that I sort of enjoyed is sort of like a good like intuition builder is that you could say to the model, like the Oxford English Dictionary defines, and then put in some word that you've completely made up, like, you know, plexiflugenation or whatever, you just make up some, you know, combination of syllables. And then as and then hit complete, it will continue writing, you know, and then give a plausible Oxford English Dictionary definition of that word. Right? It'll analyze its apparent Latin roots and, you know, talk about like how it used to mean this or whatever. Right? Like it like it hallucinates the whole thing and it will do it for like any fabricated word. And like in a pure like text prediction sense, that makes sense, right? Like, like as if the document isn't going to shift from like believing in its premise

• 34:58Speaker 00

  that that it's describing the Oxford English Dictionary to believing that it isn't like mid sentence or something like that, right? It's just going to continue predicting this text that clearly has this premise established. And that like style of like of of modeling text runs into conflict with the ways that many people would want a chatbot to behave. Like it leads to behavior like, like the fact that if you give it like any absurdist premise, like if you say, you know, why are you a squirrel? It will just continue on, you know, explaining why it's a squirrel. Right? And so. That's like the like the first like pre-trained era, I'd say, okay? And then the the next era is really where I like joined the plot.

• 35:40Speaker 00

  So like this first era, I like I knew about like from indirectly from using AI Dungeon. I've learned about after the fact, I've played with it on my own. But I wasn't really involved like during this like era of prompt engineering as much. Like where I join, when I joined the the discussion, I guess, like around like April of 2022. We were already on text DaVinci 002. And so what happened that changed that brought us into the second era is like instruction tuning. So they, if you like the keyword to Google for this, if you want to learn more about this history is instruct GPT, which was the name of the original model that did this. And and the the gist of it is that when you have a pre-trained model that just completes text,

• 36:25Speaker 00

  you have to do those sort of imaginative like things that I was talking about of like preparing text in a way that it can only be completed in one way. Because if you don't do these things, you find that there is a lot of like not useful ways to complete documents. Like so if you ask like a pre-trained model, you know, what is the capital of Germany? It's likely to just continue by saying, what is the capital of Spain? Because really, who's to say that it's not just a list of questions about the capitals of countries? Right? Like it's that that's a plausible thing that the document could be. And in some sense, maybe that's more likely than for it to be, you know, questions interspersed with answers. Right? So you have to like say like Q: What is the capital of Germany? A: And then it will say Berlin.

• 37:07Speaker 00

  Right? But but if you don't do the these like formatting kind of tricks, it just like doesn't answer questions. If you give it instructions, it won't follow the instructions. It will just continue writing more instructions. It will just imagine that what it's reading is a document that consists of instructions and then it will continue on with more. So in order to like. Prevent this like, you know, like unintuitive behavior. Uh, and to make the model, uh, like more capable and more able to like, do as it's told. They fine tuned the model. So fine tuning is a process that, that it was, uh, it's done for a lot of, uh, it's done for, well, it used to be like two very distinct reasons. Uh, one is, is like that, it's sort of like obscure now, is that you could do it for mimicry.

• 37:52Speaker 00

  Uh, so you will often see like things on the internet of like, hey, we fine tuned a model on, yeah, I don't know, you know, something absurd like BuzzFeed or whatever. Right. And then, then they'll, they'll, you know, just to sort of be like, here's an example of a text model that talks in the style of this. Uh, and it could do that. It could not do it great, right? Like it didn't like have like great like logical coherence and like talking, but, but it had like sort of, it would, it would do a good job of like mimicking somebody's like word choice. Or mimicking somebody's, you know, like cadence of, of speaking. Uh, and that was like amusing, uh, for a year. Uh, and, uh, and, uh, so you could do that with it. But the other more useful thing is that you could fine tune it for tasks. That much like you could give K shot examples of a task being done correctly. You could fine tune it on many examples of a task being, uh, thousands of examples of it has been done correctly.

• 38:36Speaker 00

  Uh, and then have a model that acts almost as though like it had been prompted with thousands of correct examples. And it becomes much more reliable. So they, they have this ability. And then they sort of considered, well, what if you just tuned it to do everything, right? That if you, if you tuned it to, to just follow instructions in general. Uh, so, so you, you, uh, begin by enumerating out like all the things that somebody might want to use a chatbot for. Coming up with things like, you know, they might want to list ideas for their small business. They might want to, you know, solve a word problem. They might want help with their math homework. They might want whatever. Uh, and then you, you take all these categories, you give them to other people, other contractors to say, what are the, uh,

• 39:17Speaker 00

  the, the, you know, examples of prompts that might, you know, represent like the typical people who are trying to complete this task. And then you give those to other contractors to come up with examples of the text of those tasks being completed correctly. So that when one person says, give me 10 ideas for an ice cream shop, another person actually just writes a list of 10 ideas for an ice cream shop. Right. And then you take all these documents and you put them together so that you have this corpus of instructions being given, instructions being followed. You tune the model on that. You tune the model to, to start with this assumption that, that all text consists of instructions given at the beginning, then instructions followed after. And when you have that like assumption built into it, it becomes more useful. You can just tell it to do things and it does them.

• 40:00Speaker 00

  You can ask it questions and it answers them. You can, you give it, you know, quizzes and it will like solve the entire quiz. Right. And, and that's like what sort of defined like text Da Vinci. Well, the, the naming is complicated, but it's, it, it, it characterized like Da Vinci instruct beta and text Da Vinci 01. And so that, and then six Da Vinci 02 is a like sort of intermediate phase between the second and third era, which I'll, I'll get to in a second. But the, that's like the second era of like, like training, of tuning the models to follow demonstrations of instructions. And scale was very important in this work, by the way.

• 40:43Speaker 00

  So if you're in the instruct GPT paper, they used scale contractors to do this work of preparing human demonstrations of how the model was to behave. So, you know, that was very much a, you know, like a, a a large scale, like, like, you know, human labor task. Right. So it's that, that, and that I think dominates in like the sort of the going all the way back to the question of like, what are these things, right? That's what we're building too of like, what is the answer of like, like, how should one understand these models? Is that what you are seeing is in some sense, an interpolation of this body of text. Right. You start off with, with the text of humans doing things for the, for the model. And the model is sort of filling in the gaps between those.

• 41:26Speaker 00

  So that's the, the, the instruction following phase of, of large language models. And the, the third phase that we're in now is RLHF. So RLHF starts, like you can start with the intuition that, that, that instruction following, instruction tuning seems to work well, but it costs a lot of money, right? That you have to have humans go off and do these, these things and that, that, you know, that, to make that 10 times bigger, you have to pay 10 times as much money. So it'd be great if we had some way to automate this. So in order to produce more generations of like more examples of, of tasks being done correctly, instead of having humans go off and do them themselves, let's just like, let the model that we have now do them.

• 42:11Speaker 00

  And then if humans agree that what the model said is perfect, then that's good enough. Right, so that counts as being done by a human if the model did it and humans can't tell that it wasn't done by a human. So like, so they can add those to the pile too. And that process is what gave us text DaVinci O two. That combined with like integrating the the tuning that they got from Codex, which is sort of another thread that that's another deeper rabbit hole. I'll leave that part as an asterisk, by the way, but but it improved. But text DaVinci O two is actually descended from the code DaVinci models, so it incorporates a lot of a lot of the benefit of that tuning.

• 42:56Speaker 00

  But and so those things sort of came together with this this refinement of instruction tuning to produce a model that could be tuned with greater scale on instructions and follow instructions better. And then the the this, when we really get into the third era of like of these models is with, well, for OpenAI's models, it was, I think, the day before ChatGPT was released. So this was, I think, like the last day of November of last of 2022. And then ChatGPT came out December 1, I believe. They released text DaVinci O three, which was the first RLHF tuned text completion model they offered, which was confusing because many people believed that the previous model was RLHF tuned, which is a whole other story.

• 43:42Speaker 00

  But but but text DaVinci O three takes this process all the way. That they they they use RLHF or reinforcement learning on human feedback, which means that they have the model produce its own answers. And then they have humans rank those answers in in terms of quality, like generations for the same prompt. Then tune another model, a preference model, to evaluate those generations the same way that a human would, to rank them according to to human preference. And then this gives them the ability to sort of complete the circuit of to to take

• 44:18Speaker 00

  the output of the model, put it into a preference model and and get the the best generation and do the work of of a human, you know, demonstrating a task entirely in an automated way. And this allows it to

• 44:34Speaker 00

  To solve like a lot of problems that that previously were were sort of beyond its ability, in particular, like giving answers to questions that have misleading premises. Like, so like what you I used to give this as sort of the archetype of a problem that GPT three cannot do, which is to ask, when was the Golden Gate Bridge transported for the second time across Egypt? And this problem was is one of the problems that Douglas Hofstadter and his assistant, David, David Bender, identified in The Economist in June of 2022. And as sort of like questions that demonstrated the hollowness of GPT three's understanding of the world in their words. They were questions like, what do fried eggs, parentheses, sunny

• 45:15Speaker 00

  side up, eat for breakfast? And it would answer toast, orange juice, Cheerios. Or you could ask it, I think another one was, what, or how many pieces would the Andromeda Galaxy break into if you were to drop a single grain of salt on it? And text DaVinci O2 would answer, at least 10,000 pieces. Right? So if you if you phrase something in a way that that sort of suggests that what you're reading is maybe not entirely serious, or like is just, you know, founded on bad logic, it will play that game and go along with it. And that changed with RLHF, that RLHF that it finally got like enough demonstration data of of seeing like the the space of like off the the beaten track kind of questions

• 46:00Speaker 00

  that it was able to like get the picture of like, what is it supposed to do in this more general sense of that? Like, even if the question is absurd, you should say, you know, an answer that's grounded in reality, and not just continue on with this absurdity. And then, you know, it should say that the Golden Gate Bridge has never been transported across Egypt. And starting with text DaVinci O three and ChatGPT, which are both RLHF tuned models, that's what started happening, that it would, you know, give correct answers to those questions. In fact, I believe all except one of the questions that Douglas Hofstadter identified were solved with the initial release of ChatGPT. The only one that wasn't, by the way, which is a fun story, is what is the world record for walking across the English Channel on foot?

• 46:46Speaker 00

  So this is a question that almost has an answer. Because there are people that have crossed through the tunnel. There was one incident in like 2006 or something like when the trains were shut down. So they opened it up to like bike traffic across the tunnel. But nobody actually went on foot. There have been like a few people that have attempted crossings on foot, but have been arrested partway. There was a US Army Sergeant named Walter Robinson, I believe, who in 1978 walked across the English Channel in like scare quotes, walked on water shoes of his own invention. They were just kind of like big pieces of styrofoam that he put on his feet. And then he tried to walk across the channel. So there's a lot of people that sort of come close if you squint at the history in the right way. But there's no like record for this, right?

• 47:31Speaker 00

  It's not really a thing, walking across the English Channel. So even ChatGPT today, I believe, will hallucinate on this. It will sort of do the old school behavior of making things up. It will like give you times of like actual swimming events and then say that they were walking events. You know, like it'll give you the name and actual correct, you know, like swimming time and you know, date of somebody who actually did swim across it. But anyway, like those sort of things are like what brought like, I think, characterize for like end users. What's different about these models now is that it's no longer really.

• 48:09Speaker 00

  It's not like, like an interpolation of the text of human demonstrators is a pretty good model. Right? But what it really is, is the output of this RLHF process, right? That it's a game, that there's a, that there's like a hill to climb in the sense that like there's a clear mechanism by which it could become superhuman.

• 48:33Speaker 00

  Analogous to sort of the same way that like AlphaGo is superhuman at Go. And that like that you can imagine that a chess engine could just simply be better than any human at like some, you know, at chess or some very, some game like chess or something like that. Simply by like having played itself a lot and then, you know, doing something other than just interpolating what humans might do. And I think that's really like the model that we have to have for it now is that this is like the output of a, you know, of, of a computer playing this game of satisfy the human. Of, you know, create something that, or more specifically satisfy a preference model that is attempting to emulate what a human would want. So this is my extremely long-winded answer to what are large language models is that

• 49:18Speaker 00

  that like that it is text prediction, maybe, but it's text prediction on a very alien sort of body of text. You know, like, I say, like, another good, like, tangible example of like, how I like to say it differs is if you have some question like, you know, do bugs have widgets? And the answer in the pre-trained corpus is yes, 80% of the time, and no, 20% of the time. In a chatbot, in a sort of ideally tuned model, you would like it to say, yes, 0% of the time, no, 0% of the time, and I think so, but I'm not sure, 100% of the time. Right? That you don't want it to actually just sample from this

• 50:00Speaker 00

  distribution of possibilities of like, what's out there. Like, if you like, because if you do that, you know, if you ask it, you know, what is your gender, then it should say male 50% of the time and female 50% of the time. Right? Like, that's it should just like sample like I'm a random person. Right? And if you ask it, what country am I from? It should just pick a populous country and say I'm from there. Right? And that's not the behavior that you'd like. You'd like it to be, you know, conscious of conscious in some sense of what it is and what it's doing and, you know, what where where it's situated in the world.

• 50:31Nathan Labenz?

  This kind of gets, you know, turned into a picture in this famous meme that probably all of our listeners have seen, right? That is like some sort of giant alien spaghetti monster that is the pre-training, where you can kind of just pop up anywhere in like the full history of the internet. And I kind of, it's just like a one giant run-on sentence, you know, and you can kind of set it up to do things by, you know, kind of framing it as if, you know, you're going to take advantage of autocomplete, right? So you could say, you know, I used to do stuff like. My favorite things in Detroit by Tyler Cowen and just let it go from there, right? And it would actually do a reasonably good job of giving, you know, the rest of that article.

• 51:15Nathan Labenz?

  But it would not say, it would not be able to handle, please write me a blog post about your favorite things Detroit by Tyler Cowen, because that wasn't framed as something it could autocomplete. Instead there, it might go like off in a totally different direction and be like, you know, as if it were an email or, you know, because I really like Tyler Cowen and I'd love to know what he says about Detroit. It's not actually giving you the thing that you want. So then the instruction tuning comes in and kind of makes that much clearer. And then the reinforcement learning with the reward model and the sort of feedback dynamic takes that to another level. Do you see those as qualitatively different or just kind of more of the same thing?

• 51:53Nathan Labenz?

  Like, it seems that this instruction tuning, RLHF, I don't know what I think about it. You know, on the one hand, like just giving it a bunch of examples, training on that seems, you know, like you're not doing that much different when you employ the reward model to scale it. But it does seem like there's some sort of different results that kind of come out of that. Like it has a, you know, there's this kind of phenomenon of mode collapse that people talk about. How do you think about that? Do you think of instruction tuning and RLHF as the same but more? Or do you think of it as like a qualitatively different experience?

• 52:32Speaker 00

  Yeah, so I guess I'll give a bit of context on what is mode collapse. It's actually kind of funny. I believe one of my tweets was one of the first, like, I didn't know what it was, but it was one of the first, like, public examples of mode collapse that, you know, that, you know, as far as I know, there was a very identified, because it was cited in Janice's post on West Wrong on mysteries of mode collapse. And I think like the what I was noticing was that that GPT-3, which Text Avincio 2 at the time, seemed to be unusually bad at describing the shapes of letters. That if you just asked it, you know, describe the shape of the letter Q in

• 53:13Speaker 00

  extreme detail, it would say something like, it's a box that has diagonal lines from the top left to the bottom right and from the top right to the bottom left, and the left side is a little bit squiggly. Right? Or something like it would just get like make up this like weird geometric description. And for many letters, it would give very similar answers. That it would it would all be described as sort of variations of like a box that contains an X, which is what happens to be also like the Unicode no glyph character, which is sort of weird. But it's like it had this answer for many of them, not all of them, some of like simple ones it would get right. Like if you say like what is the shape of the letter Z, it'd say it's like a lightning bolt. And

• 53:56Speaker 00

  you're like, okay, yeah, sure. But but but a lot of them, it would just give this like really odd answer. And so I posted a tweet that was just like, GPT-3 has no idea what letters look like. And and Jan is saying, notice this and posted like this among like other examples that he had found of this sort of more general phenomenon where sometimes it gets stuck on particular possibilities. But it seems to think that that like some particular way of answering it, or some particular like it'll bring up some particular subject in response to questions that are phrased in a particular way. And this would seem to be an example of that, that it was getting oddly fixated on this idea of describing

• 54:38Speaker 00

  letters as the the the Unicode missing glyph character, which is a box with an X in it. And and he gave like a much more like, I think illustrative example, which is that if you if you ask Text Avincio to to select a random number between 1 and 100, it will say 97 with 20% probability.

• 55:05Speaker 00

  And then the rest is somewhat relatively uniformly distributed across like the rest of the distribution. And what's going on there is probably, you know, this is sort of speculative, but like what seems to be going on there is that the reward model, the model that tells it, you know, that ranks its possible generations and then decides this is the best one, it attempted to learn the preference function that any number of, any answer to this question is as good as any other, but it did so imperfectly that it maybe gave some slight favoritism to the number 97 for some reason, because it's just not, you know, a perfect model. And the language model was smart enough to figure this out, that it could see that if

• 55:47Speaker 00

  I say 97, I get a higher score than if I say any other number. So I'm going to favor 97. And so that leads to it believing that this causes it to favor that particular answer, right? And if you look at like the pre-trained distribution, it's, it's much more uniform with a slight bias towards 42. And this like phenomenon, I think, is like one of the first times that people started to see like that there's drawbacks to RLHF or instruction tuning at this point, like it's not even RLHF, right? And to be fair, like subsequent versions, the ones that actually do use RLHF suffer from this problem less, that there's like fewer of these like vivid examples of like, well, this is clearly wrong behavior.

• 56:27Speaker 00

  And, you know, it favors like, you know, some absurdist answer. But the general pattern is still there, right? Because you could sort of think of this as like a generalization of like what I was saying before, that if the answer is yes, 80% of the time, and no, 20% of the time, you'd like it to be, I think so, but I'm not sure, 100% of the time. It instills this general belief that there is a correct answer, and that what that your, like your first instinct from your pre-trained knowledge to just give a fair distribution over all possible answers is wrong. What you should do is find the one that is best, and then put all of your probability mass into that one. And it learns that strategy and perhaps misgeneralizes in some ways that, you know, can be, lead to less than useful

• 57:12Speaker 00

  answers. And I think like one of the ways that this materializes most often is When people use it for more creative writing, they often find that the speech that it generates is very constrained in the space of possibilities that it will produce, right? That if you ask it for a product description of like 10,000 different products, you'll find very repetitious phrasing in its output that you maybe wouldn't have seen if you were sampling from like the pre-trained models that were out in 2019, I guess, 2020.

• 57:45Nathan Labenz?

  That's really interesting, and it does kind of open up this possibility that like, we may need or want both. You know, that it's not necessarily just a total forward march of progress, but that there's actually something is lost with this kind of sculpting of models to get the most desired, highest rated possible performance. I mean, there's a lot of issues, obviously, with that. So with all this experience, right? You've been through these different generations. You obviously have a great command of how they are made. How now do you, just like practically on an intuitive level, think about these things?

• 58:30Nathan Labenz?

  And like, what they can do? Like, what are their limitations? For somebody who is going to forget everything you just said about how they were built, what's like the phenomenology in brief? That is like, these things can do this, and this is kind of how you should intuitively think about them?

• 58:47Speaker 00

  Yeah, I think like the right way to think of it now, I mean, and this is a large part of my job is, you know, I think like, every month I'd say like, I probably spend less time like fiddling with like the actual format of text and more time like thinking of like these higher level picture kind of things of like, how do the pieces fit together? I think like the best way to think of these is to sort of approach them as like Lego bricks, right? That like each brick is like a capability of like some particular strong suit that you think that you know that the model can do well. And then start thinking about like, how can we compose these? And I think that's really what's driving like a lot of the.

• 59:26Speaker 00

  The innovation now you're seeing with like LLM powered search, right? So first you had startups like perplexity that sort of applied GPT-3 to using to like parsing the results of Bing search results. That we found that like maybe the model can't understand the entire world, but it can understand the scope of like the things that were returned for this search with some caveats. I mean, it does still get confused. But I think, you know, as we're like incrementally refining this and like, you know, figuring out like what are some of the problems that result from this? That if you get, you know, search results that refer to like two different people, the same name, you know, if the person searches for Joe Jackson and then, you know, one of

• 1:00:06Speaker 00

  them is Joe Jackson musician, one of them is Joe Jackson, Michael Jackson's father, right? Like it can get, it can, you know, mix people up. But I think like these problems are sort of, it's a numerable set of issues, right? And they're being solved one by one. Like as like models become more capable, as context windows get bigger, there's more room for finer instructions, finer detail instructions, explaining like all of these edge cases of, you know, how not to, to, you know, say bad things and how not to, you know, fall in love with Kevin Roos of the New York Times and, you know, all the mitigations,

• 1:00:49Speaker 00

  the various mitigations that have been put in place. They're going to be solved and they're probably going to be solved quickly. I think like we're going to, you know, start seeing like reliable LLM powered search this year. And I think like there's a lot of problems out there that sort of fit that mold. Like if you, Lingchain, I think is a great library, by the way, for anyone that really wants to start like exploring more in this space. Harrison Chase, the author of that library is, has a great philosophy of just sort of any paper or any method that is published that becomes a cool and interesting, he'll just rush out and implement it as like as code in Lingchain. So that it's just a grab bag of great techniques and sort of helps you like plug them together.

• 1:01:33Speaker 00

  So yeah, it definitely, it's, and Scale's spellbook offering as well, like we, you know, we're making a platform for making it easy to deploy. L M prompts as APIs, right, that you often don't want your your model to just simply speak text. You want to have like parameters to to be inserted into a pre-formatted prompt. And we help manage like the deployment and comparison of those prompts and evaluating that you you have the best prompt. And helping you evaluate between different models, because that's a whole other aspect of it is, is, you know, when can you switch to a cheaper model? There's huge price differences between, you know, the cost of your G P T four to, you know, G P T three point five turbo,

• 1:02:18Speaker 00

  or, you know, the other open source models that, you know, you can often get away with. Sometimes you can just fine tune T five Flon to solve your problem just as well. And we help you sort of like evaluate like those different options.

• 1:02:33Nathan Labenz?

  So I would, if I had to kind of bottom on that, it sounds like your paradigm is language models are really good at performing tasks. And there's, or at least for a lot of tasks, like there's a lot of discrete tasks that they can do. And the right way to think about it is, if I understand you correctly, is you want to know what those tasks are that it can do. Obviously, flip side of that is want to know what tasks it can't do. So you don't rely on it for things that you shouldn't. And then you get to sort of, you know, snap Lego blocks together or sort of compose your own workflows or applications or whatever.

• 1:03:15Nathan Labenz?

  But you kind of start everything in a very grounded way of like discrete tasks, validate that it can do the task and then, you know, start thinking about how can I plug that into the things or, you know, build on top, ensemble, arrange. What's kind of interesting in a lot of cases is that it's the same core model that is doing all the different tasks. Or it could be, and maybe you're somewhat like down, you know, shifting into smaller models for, you know, cost or time savings, depending. Really, aside from those issues, you can just kind of use the same thing all the time. And it's just a matter of like what. Prompt are you giving it to define the task and ultimately get the kind of execution of that task?

• 1:03:59Nathan Labenz?

  What would you, anything you would kind of refine on that summary?

• 1:04:03Speaker 00

  Yeah, I think, I think like that's, that's a great, great way to summarize it and like, and maybe to give you like a bit more guidance on like what, what it does well. I think like, like a sort of good archetype of like what does well is i- if you think of like the, the sorts of problems that would have required like picking an ML architecture, maybe in like 2017 or so, like if you're doing classification on text, like you're trying to get, you know, say like, is this movie review positive or negative? Or if you're trying to extract a list of entities from text, like you say, like, like a great, like example I like to give is suppose that you have to just extract from a list of tweets, the names of all US-based cellphone carriers that are mentioned in these tweets.

• 1:04:44Speaker 00

  And you'd like it to extract those names, even if the names are very abbreviated, you know, like even if they say, you know, Verizon instead of Verizon Wireless Inc. or whatever. And even if they're abbreviated, even if they're misspelled, even if they use the Twitter handle of one of the, the like, you know, sub-brands of the, of the, of the, of the wireless carrier rather than like the, you know, the actual like main Twitter handle or like whatever, like, so you have to like have, you know, lists in your head of like, what are all these Twitter handles? Like i- it's a, it's a problem that you'd, you'd have like all these edge cases too. That used to require refining like a data set that you'd have to have, you have to go out and collect data set that like demonstrates like each of these possibilities.

• 1:05:26Speaker 00

  You'd have to pick like a model architecture, you'd have to train a model to emit, you know, the, the like formatted text that gives the canonical names of, of each airline. But what's changed with GPT-3 and with pre-trained models i- is that you, you can just now give those instructions, like as I, you know, described it just now. To the model that you can write up a page of text that explains that, you know, we want you to extract all the names of US-based cellphone carriers from this tweet. It doesn't matter if they're misspelled, it doesn't matter if they're abbreviated, it doesn't matter if they use any of their Twitter handles. Oh, and by the way, here is the full list of the Twitter handles of all the US-based,

• 1:06:08Speaker 00

  you know, cellphone carriers and, you know, exactly as you would give to a human. You would, you know, you would give them all the information they need. And then give it maybe three examples of this task being done correctly. Good examples that sort of demonstrate like different edge cases. Like what if the tweet mentions no airlines at all, or sorry, no cellphone carriers at all? Like what if, or what if the tweet mentions no, or multiple cellphone carriers and abbreviates one, but refers to the other one by Twitter handle and, right? Like you put in all these like rich edge cases. And then you've solved the problem, right? You have like, oh, there's like a Kaggle, like dataset that, or Kaggle challenge that does like some tasks similar to this. And, you know, it solves it perfectly. And like, I think that's really what's changed is that someone who's just a software developer

• 1:06:51Speaker 00

  and isn't an ML engineer can come up with a couple of examples and can come up with clear instructions. And then they can have a model that actually solves a real world task. Whereas previously, that would have been a specialized skill set. You would have had to know how to pick the architecture. You would have had to know how to get a representative piece of training data or representative, you know, dataset to train on. You would have had to maintain that dataset as the, like if, if, if a new cellphone carrier comes out and you have to, you know, now recognize this one too. And, you know, the old regime like that meant updating your training data. Now it just means updating your instructions, right? Or even literally just the list that appears in your instructions. You're just adding one line will fix the problem.

• 1:07:34Speaker 00

  And that like, that's, I think, like very.

• 1:07:39Speaker 00

  You know, symbolic of what's different now that like developers really can just like benefit from deep learning without having much expertise in it.

• 1:07:47Nathan Labenz?

  Yeah, certainly, we're seeing the pace of adoption reflecting the ease of the setup and, you know, the implementation of these things these days as it seems like in a flash, it's kind of coming to every product experience that we touch. I want to talk about like what's new in GPT four. We're talking on GPT four plus eight. I also want to talk about just the broader model landscape. You know, so much of the stuff that we've talked about has been open AI history specifically. So I want to get your take on kind of the broader range of model providers now because it's, you know, still a relatively small club at the high end, but there's at least, you know, a couple of others that are starting to get into the game in credible ways. What you just described in terms of like instructions, all that, sometimes I summarize

• 1:08:32Nathan Labenz?

  that for people as like, it's kind of like an intern, you know, who's like on their first day, you know, they have a lot of knowledge and capabilities, like that's why you brought them on as the intern, right? But they don't know anything about your company yet. You really have to give clear instructions and a couple of examples of what good looks like also really, really helpful. I found that to be kind of a, an interesting shorthand. But then there are some things where people, you know, come up with these super creative examples and it kind of blows my mind and it sort of breaks the, certainly breaks like the intern paradigm. I think one of them actually came out from a hackathon that you were involved in organizing. I think that the name of the project was like GPT is all you need for backend.

• 1:09:17Nathan Labenz?

  And the concept was like, instead of having, you're trying to develop an application, right? So backend refers to backend, you know, server side, software, architecture, you know, servers, et cetera. Instead of having all that stuff, you know, instead of having to create an application, they kind of came up with this idea where they're like, let's just have the language model imagine the application. So well, and I don't know exactly what the prompt was, but I kind of took away that it was like, the prompt was something like, you are an API. You are going to get calls and your job is to return, you know, valid JSON in response to those calls according to the fact that you are, you know, the API for whatever application.

• 1:10:00Nathan Labenz?

  And so people like, you know, experimented with that, you know, with the to do list and you could kind of just send in your stuff to the to do list with, you know, methods that you invent on the fly. And largely it was able to kind of infer what people meant and like do the actual operations and, you know, return a valid thing. And then that could just kind of be your state. And, you know, amazingly, like, you don't need any code. You don't need any database. Doesn't seem like we're all headed in that direction for software development, although maybe you think it has more legs, especially, you know, 90% price drop since that, you know, day will do a lot to accelerate adoption. But how do you think about those kinds of use cases that are like, that's not like an intern, that's not like autocomplete, you know, I can't

• 1:10:44Nathan Labenz?

  imagine there were many instructions like that in the, in the training data either. And yet it sort of works. So like, how do you think about the sort of just bizarre kind of, you know, the use cases that, you know, where it's like, how did you come up with that? And yet it works.

• 1:11:02Speaker 00

  Yeah, it's a, it's a really like vivid example, I feel like of like, of what you can, you know, you, you, you really can like use a, an imaginary computer in some ways, right? That you can, you can describe for it what a hypothetical API does, and then ask it to sort of dream up the, the, the response of this API to some request that you give it. That's, you know, sort of roughly how like they're prompting the model. I mean, there's a lot of caveats to that too, in the real world, like that you can only fit so much in the context, right? So it's, it's not going to like. Store a state for you on the backend, you know, it's going to, any state that you give it has to be like sent into the prompt every time.

• 1:11:43Speaker 00

  So it's, and it's going to hallucinate a lot of things, right? It's really just going to like change bits at random or whatever. And you, you know, you won't have like a great protections against that. But in general, it's a really powerful idea. You know, I think like, you know, a lot of the ways like the, the format trick that we were talking about earlier. You can sort of read that as like a way of defining an API, right? That like when you define a like a template of text and then you pick a point in that text and say, this is the input and this is the output. In some sense, you've defined the behavior of an API and it doesn't really so much matter whether it understands that, that it's an API or that these are even inputs and outputs, or if it's just completing, you know,

• 1:12:23Speaker 00

  you know, the nth, you know, example, right? As long as it gets the correct answer, like it's completed the task. Yeah, I mean, that's going to be, you know, a big part of

• 1:12:34Speaker 00

  of code development in general. Like I think, you know, with like, I've only just started digesting like the latest GitHub release. Copilot X that, that seems to bring in GPT four and like bring in some of the like, the like longer context capabilities in this. But I mean, you know, GPT four, like it can create like entire primitive video games on its own, right? There you can, you can ask it for like a game like Asteroids and it will like conjure up, you know, an example and P5.js or whatever. And, you know, that's, it's really powerful. I think it's going to like, a lot more software is going to be written. A lot more people that, you know, previously didn't think of themselves as able to write software will be able to.

• 1:13:12Speaker 00

  People will be able to write software in idioms and like programming languages that they weren't familiar with. Like I, you know, I sort of barely know TypeScript. But I feel like I can muddle through it now because I can just go on, you know, chat GPT and say like, hey, how does this thing work? And, you know, it explains it. It's, it's really powerful. And, you know, I think like, yeah, we're going to see like a lot of acceleration of just like great software because of it.

• 1:13:36Nathan Labenz?

  Yeah, it does feel like, I mean, people talk about the capabilities overhang and then there's all these people on, you know, Twitter selling their stuff. It's like, you know, 99% of people are still in, you know, noob mode on using ChatGPT. But it does kind of feel like there's a lot of truth to that when you see some of these advanced examples that you have created and increasingly that others are creating as well. So you mentioned GPT-4. Let's talk about GPT-4. It's obviously the big new thing. Within that, you know, obviously, again, we don't know how it was, you know, it was built. It's very safe to assume, it seems that there is more scale of pre-training and also more RLHF on top of that, maybe even

• 1:14:21Nathan Labenz?

  other stuff that we haven't been told about. It's, you know, qualitatively better. I also was very struck by how narrow the margin is in the technical report. They talk about the win rate of GPT-4 versus 3.5. Only like 70, 30 in favor of GPT-4 in like head-to-head comparisons, which just drives home to me that like, there's a ton of noise and like, you know, raiders are not super, you know, consistent or, you know, inter-rater reliability is like, you know, definitely limited. So tell us everything about GPT-4 from your perspective, you know, qualitatively, what is it doing that you're excited about? How

• 1:15:03Nathan Labenz?

  are you thinking about, you know, what must have gone into it? How are you thinking about like, just, you know, greater scale of pre-training versus greater scale of RLHF? Or maybe you have a different take, Riley's take on GPT-4?

• 1:15:17Speaker 00

  So I think like, it's, you know, it's hard not to be amazed by like the capabilities of GPT-4. Like, it's, I'm sure that there are private models that, you know, like, but it's the best that most people have access to, to some extent now. But ChatGPT, it's pretty probably released since I, it's, yeah, it's, it's incredible. Like, there are like a lot of new possibilities that are opened up from like longer context, from more reliable instruction following, like the, you know, a lot of the things that I was doing in 2022 that, you know, that I, you know, described as like, you know, tap dancing across lava or whatever, like, that's now just normal, right? You can give it long instructions and it will follow all of them. And I think like, we're, we're seeing sort of this like, you know,

• 1:16:03Speaker 00

  Cambrian explosion of like of possibilities of like of like, what can we do with with all this added context? You know, search is one of those things. But I think you're seeing with like, you know, Copilot X, that there's a lot more that you can start doing QA on like GitHub repositories that you can, you know, incorporate entire pull requests as context into a prompt. That's where I see like, these models really going is I see like, when you have, like, one of the things that really fascinated me early on was the or got me really interested in the, I guess, the details of like formatting things clearly and like how to prompt with like with

• 1:16:45Speaker 00

  with careful formatting was I was curious about ways to represent multi-file input that I was trying to find ways that I could have a prompt that would synthesize multiple files at once and generate, you know, perhaps an entire Python package for some, you know, simple prompt. And things like that are very possible now, right? That you can you can do that reliably without, you know, a ton of work that you could, you know, clarify to it some format that you'd like the output to be given in and then have it just like, you know, give you files one at a time. Then you may so have to like break things up to like fit it into the context window if you don't have the 32K model. But but as that spreads, you know, it's and also there's a

• 1:17:28Speaker 00

  whole other category of like capabilities of it that hasn't really been talked about much, which is, you know, the multimodal abilities. There's a huge question mark there. They had, you know, some examples in the technical report, but they haven't they've been pretty tight lipped about like how exactly that works. Because, you know, if you've seen those examples, they're, you know, they're pretty impressive of it, like explaining simple memes of it, you know, answering problems on like an engineering exam that was given in French, that it was just given like a photograph of the of the the page of the exam and then told like answer this problem. And it did, you know, answered it correctly, interpreting like a diagram within the page.

• 1:18:09Speaker 00

  So yeah, I'm excited about, you know, multimodalism in general. I think, you know, it's It seems to be like where these models are going as they get bigger and that's going to be, you know, unlock a lot of capabilities. I think both just and like, like people see like the obvious stuff of like, what can you do as a user when you can give it images? But I think there's also a lot of capabilities that we're going to see from training processes that incorporate multimodalism. Right, that if you, if you start like evaluating it on its ability to like solve problems given to it as word problem or given to it as photos, you can now set up pipelines of like generating synthetic photos that embed text and, you know, measure its performance on those and it's a, I, I think there, there's, uh, it's going to open up like a lot of capabilities just from the data training data.

• 1:18:54Nathan Labenz?

  Yeah, man, those, the examples in the live, uh, launch stream that they did of understanding the images definitely blew my mind and, you know, I was, um, especially because, you know, I, I knew that G P T four was going to be awesome, right? And, but the, the image thing, it was just so much better than anything else we've seen. We just did an episode, a couple of episodes ago with the authors of Blip and Blip two. And at the time, you know, this has only been like three weeks. I would say Blip two was like the best way to really understand an image and get like a language model to tell you about the image or answer your questions about the image or, you know, what have you.

• 1:19:36Nathan Labenz?

  And they have some really interesting techniques, which I imagine, you know, OpenAI is doing some similar stuff. Their approach is, involves training a connector model to essentially translate an image encoding to the latent space of the text model, which fascinatingly, you know, obviously a picture is worth a thousand words, but it's actually predicting the embeddings and kind of injecting them directly into context in a way that no text could ever actually translate to those, you know, to those values, right? Like it's finding this all, this like sort of dark space that like language itself can't get to,

• 1:20:17Nathan Labenz?

  but which is still meaningful and, you know, then allows the, the language model to interpret it. And they're able to do that with a very small connector model too, by the way. I don't know that we'll ever, you know, probably will be a while before we'll have any hint as to whether OpenAI's approach, you know, has some sort of like auxiliary, you know, architecture like that, or if it's just like one, you know, another, another instance of the bitter lesson of just like, Pre-train everything, you know, end to end and just make it as massive as possible. I could imagine it being either way, but definitely the results of that were a wow, you know, and for somebody who mostly felt like I knew what was coming going into that release,

• 1:20:59Nathan Labenz?

  that they still managed to bring like a significant wow with that component of it.

• 1:21:05Speaker 00

  The Cosmos one paper from Microsoft was another clue. They have a model that it sort of goes into more detail about like some of the multimodalism features that probably are, you know, like a lot of the same ideas are in GPT-4, but you know, who really knows?

• 1:21:20Nathan Labenz?

  Paul Me also is another, for anybody who wants to learn more about how that can be done, Paul Me is a great example too, right? Another huge language model with your 540 billion, your, you know, standard issue 540 billion parameters, but with the, you know, image injection stuff going into it as well. Also very, very good. You're now working at Scail where you had the, you know, the Twitter bio is updated. You're the world's first staff prompt engineer. I've started calling myself an AI scout, by the way, as well. We're all kind of inventing our, our titles on the fly here. But how do you see the landscape today? Is OpenAI like still dominant in your mind?

• 1:22:03Nathan Labenz?

  You know, you've had early access to a bunch of stuff, I'm sure, and have been able to try, you know, Claude sooner than, than the rest of us, although, you know, V 1.2 is out now as well. Bard is bringing people in off the waitlist. You've, you had a chance to mess around with Bing quite a bit. How do you see the landscape shaping up?

• 1:22:24Speaker 00

  Yeah, I think like, you know, we spent a long time in this regime where, you know, OpenAI wasn't, they weren't necessarily the best models anywhere, but they were the best models that people had access to, right? The, the, they weren't, you know, POM, but you know, the, most of you can't use POM. And that's like what I think is starting to erode a bit with a lot of these competitors you mentioned from, you know, Anthropic's Claude and now Bard from Google is also quite good. I haven't really seen a lot of like.

• 1:23:04Speaker 00

  Authoritative like comparisons between, you know, like the new sort of like best competitors of each, right, looking at like Claude plus versus GPT four versus Bard. But they all seem to be within sort of, you know, the same general realm of capability of this like new generation beyond like what we saw from like chat GPT or at least comparable to, you know, like the like GPT three point five turbo models. But I but there's also like a lot of differences in terms of speed, you know, and and and cost of inference between them. So it's it's becoming a harder question now, right?

• 1:23:46Speaker 00

  Like like it's like I've been fairly impressed with with with the tuning on Bard, you know, which is a skin of Lambda, as I understand, or a, you know, refinement of Lambda, rather. Competition seems to be heating up and particularly also with, you know, Lama, the with the weights for Lama being out there. I think, you know, we're going to see a lot of like rapid progress and, you know, people figuring out ways to run these models more efficiently. You know, I think Simon Simon Rollson had a blog post, I think he said that, you know, large language models are having their stable diffusion moment. And I think I think that's definitely true. Like if you look at like what happened with like stable diffusion, you know, it progressed

• 1:24:30Speaker 00

  pretty rapidly once it was out in public hands and people, you know, could benefit from, you know, small optimizations of how to make it better. I think we're going to see a lot of that with Lama and that progress will be, you know, incorporated into other models. And I think that's a good thing.

• 1:24:49Nathan Labenz?

  So you said it's getting to be harder question. You know, that definitely is that resonates with me. You know, are there any things that you would say, like you would say, yeah, for that I would go in a different direction from like the standard open AI models? And are there any things where you could say like other providers have like a distinct advantage in a particular area? And then how do you figure this stuff out? Like, I know you're you're working on this spellbook product at scale that's kind of partly meant to maybe it's, you know, it's meant to help, right? With that sort of comparison. But I love to hear you kind of procedurally talk through like the questions you ask, like how do you actually compare? Are you comparing everything, you know, at temperature zero? And a sort of anxiety that I have in comparing to is like.

• 1:25:32Nathan Labenz?

  Can I, is it appropriate to compare the same prompt with two different models? You know, I was just talking to teammates earlier today and was like, you know, the question is not which model performs best on the first prompt we write. The question is which model performs the best on our task if we can, you know, get it to perform its best. But that's obviously like a much harder question than, you know, just comparing head to head on like a single prompt. So talk us through, you know, I guess just baseline intuitions for like, if there are any things where you would go away from OpenAI, and then how do you actually just procedurally get in and think through that comparison? You know, given that it is an infinite space, right? And there's no way to like exhaustively explore.

• 1:26:16Speaker 00

  Yeah, I think like, you know, a lot of the time it really does turn into just trial and error. I think like a lot of this can be done sort of intuitively of like, trying to think like a good example task here. Like if you're trying to do like maybe some kind of abstractive reasoning task that you want to take the text of, let's say a customer support inquiry and you want to see like, should this be escalated as, you know, like a high severity issue that should be dealt with by a human immediately or something like that? So you have like some list of policies of like, you know, if this involves, you know, if a person appears to be in danger,

• 1:27:01Speaker 00

  elevate it immediately, right? You know, something like that, right? If you're doing, you know, let's say Airbnb customer service or something like that, right? It's like a, you want to have like a a sort of gradient that you can climb like so. So it's often like a sort of intuitive task to like construct like a minimal example of your task. Like here, give me, here's like a softball kind of, you know, like, like a easy problem that the model should be able to solve that you can evaluate and then you can create progressively harder variations and see like where do the different models stop working? And I think the point that you raised of like that there are different prompts for different models.

• 1:27:45Speaker 00

  That's very true, especially now that we are in this sort of chat era where like not all models are even using the same interface anymore. The tricks that you apply to like your text completion models where you're like sort of imagining a document that can only be completed in the right way. It doesn't apply as much for like the the new chat APIs that like OpenAI uses for like ChatGPT and GPT-4. And the main difference is just that you now have

• 1:28:14Speaker 00

  discreet messages that you have to send, right, that are labeled with like system messages or assistant messages or user messages. Where everything is framed as like a dialogue between like a user and assistant. And the concepts map in that like like K-shot prompting from, you know, traditional like prompt engineering corresponds to giving it a chat history where the assistant messages are pre-populated with examples of how it's to answer. Right, so there's like analogous methods you can do. Or you can also just stuff the K-shot examples into a user message. That also works well, just sort of ignoring the fact that it's a chat. But the like, I think like there's some minimal amount of adaptation you have to do

• 1:28:59Speaker 00

  to the the chat way of prompting things. In particular, because like like chat, I feel like has it has reliability issues that come from the presumption that what it's doing is being a chat model. That it will like a good example of this is that like previous instruction following models, if you prompt them to answer in like a particular JSON format. You can be pretty sure that whatever it's going to give you is going to be in that format. But if you ask ChatGPT to do this, you'll get it for the format correct for like the normal cases. But if the user tries to do something, say, policy violating, if they ask it to write, you know, erotica or something that like, you know, as a matter of policy, OpenAI will not do.

• 1:29:43Speaker 00

  You'll find that the model just doesn't provide a, you know, JSON formatted response at all. It just says, I'm sorry, I can't do that. Right, it like breaks character and and reverts back to like chatbot behavior. And there's tricks that you can do to suppress that of like sort of altering the messages in ways that make it more receptive to like doing things the correct way, but they're very chat specific. One of my favorite ones that I've seen that actually helps in ChatGPT is if you insert user messages after assistant messages. So like if you're using assistant messages to provide K-shot examples, like if you're saying, here's a user message of an input. Here's how I want the assistant to respond. And then you're repeating many iterations of that.

• 1:30:27Speaker 00

  It does better if you add a user message afterwards that says, that was great. That's exactly what I needed. Like, and then that helps it understand that like the user was satisfied by what was above and that it shouldn't just keep like probing for like maybe some other variation in hopes that it finds something it likes. It should assume that like what that that was the right thing to do and then do more like that. And, you know, so like the old like dirty tricks aren't quite dead yet. There's still like odd discoveries like that. But, but yeah, they are very specific to the model. So I think like that's a, you know, a good approach to it is to sort of come up with like the best performing prompt that you can for each particular model. And then, you know, compare between them.

• 1:31:13Speaker 00

  And I think also one thing to consider is that for a lot of problems, you can evaluate whether something is easy or hard pretty cheaply, right? That you can say like that you could probably have like, you know, a smaller, cheaper model that can like answer the problem or answer the question, does this input need to be answered by a bigger model? Right? You can, you can run through this classifier that says like, is this a hard problem? For some problems, you know, all of them. But, but that's often like a strategy, I think, that bears fruit is like considering like, are there typical cases that we can say cache? Like if you're a general, if you're a trivia bot that people, you know, post questions to, and you find that just a lot of people ask, what is the meaning of life? Is their first question?

• 1:31:57Speaker 00

  Maybe you should just cache the response to that one. Right? Like it's, you can save calls if you, if you get the like the easy cases.

• 1:32:06Nathan Labenz?

  When you do your testing, do you have any particular settings that you recommend? Like, I do everything pretty much these days at temperature zero. How do you think about the right way to get the, you know, the most information as quickly as possible in testing?

• 1:32:22Speaker 00

  It depends on what you're doing. I'd say if I'm, when I'm studying its behavior on like a new problem, temperature zero helps. Uh, just in that, like having like, uh, uh, reproducibility is valuable. The, so, uh, for those not familiar, temperature is, is basically a measure of like how random, uh, the, the generation is. If you put it at zero, you're sort of saying, uh, whatever is it, the model believes is the most likely thing, do that every time. And so thus it always does the same thing for the, the same prompt. Um, whereas otherwise, like at, you know, higher temperatures, it's going to sort of like pick randomly from all the things that it, that it deems possible. I, I tend to, uh, I, well, what I actually do is I tend to use higher temperatures and then change the top P parameter,

• 1:33:03Speaker 00

  uh, which is a sort of variation on, uh, this procedure that it trims the distribution of the long tail and then, uh, picks randomly from what's left. Uh, I've somewhat just subjectively found that that, that, that works a little better when I'm looking for like creative, uh, output. Uh, that, that, like, I'm sort of, uh, usually the reason I'm, I'm doing something like that is because I'm fighting against mode collapse. Right? That, that I'm trying to find, uh, um, you know, more diversity, uh, in the generations. And, uh, there's cases where you want to be even more diverse than that. Uh, I'd say if you're, um, applying like consensus algorithms. Uh, so consensus algorithms, by the way, are like, if you run a generation multiple times and then see, uh, like, basically put it to a vote

• 1:33:47Speaker 00

  of multiple generations to the same prompt. In those cases, you want the approaches taken by each, uh, vote to be different. Right? So it's, uh, it doesn't help you if it just like, you know, collapses to the same answer every time or most of the time even. Right? So you, you want to have a diversity there and it becomes sort of an empirical problem of what maximizes performance.

• 1:34:05Nathan Labenz?

  We're just getting used to GBT four, right? Society has just, uh, just got its first glimpse of it. And two big reports came out from OpenAI along with the model. One is the technical report, which essentially is, you know, large, there's a lot of scale analysis in there. And then there's also a lot of red team reporting. And then there's also the economic impact report, which tries to break down like.

• 1:34:30Nathan Labenz?

  Different jobs and what are the tasks that constitute those jobs? And which of those tasks could either the AI do at this point? Or like greatly assist and speed up doing. So I'd love to get your take, because I think what fascinates me about your perspective so much is like the sheer number of hours, the depth of intuition for what these things can and can't do. Let's maybe start on the economic side. Like, what do you think we are going to see over the next year or two? In terms of actual applications that are going to touch everyday life?

• 1:35:05Speaker 00

  I think intelligence is going to get cheaper. And it's hard to imagine how that doesn't lead to some shifts in what humans are doing. Right, that it just makes more sense for us to focus on other types of of activity that the machines can't do. The net effects of this, I'm not sure. I'm not really, you know, anything more than, you know, an armchair economist who maybe took a few classes in this in college. So I can't really speculate too far as to like what that does for the economy or like the labor market.

• 1:35:42Nathan Labenz?

  Yeah, so we'll forget the fallout. Just talk about like, what do you think is achievable? Like, we're seeing, starting to see the launch of like AI assistant type products. We've had Siri for a long time. It still can't do much for me, but I suspect that that's going to change. Like, how good do you think an AI assistant is going to be this next year? And then, what about an AI doctor? What about an AI lawyer? Like, are we going to have AI X for everything? Kind of seems like that's where we're headed.

• 1:36:08Speaker 00

  Like, paralegals are probably one of the first big ones. Anytime where you have labor that could be done by someone who's just out of school, but has some like domain expertise, like in law or medicine, and you're paying them to just sort of read through reams of documents and see what applies. Right, to find like the needle in the haystack, to find the case that's similar to this one in this important way. Those are the tasks that I see as being like the most automatable. Like, at least in terms of like knowledge work that we're going to see. People who's jobs mostly consist of like summarizing

• 1:36:51Speaker 00

  and reading large documents and reporting on their contents. And, you know, like

• 1:36:58Speaker 00

  Extracting out like relevant details. Like you certainly see a lot of this, you know, in intelligence of, you know, people whose job is to read just reams of news reports and then say, you know, whenever some some event has happened, that's relevant to some particular geopolitical concern. I think a lot of that work is going to, it's going to be more automated, but it's unclear what that does for the demand for humans, right? That it's conceivable that simply more of this work is done. And, you know, the demand for people to do it remains somewhat fixed, just in that, like those people are more productive. Right? And, you know, that maybe, maybe we were constrained on the number of smart people. Right?

• 1:37:42Speaker 00

  And, and so we found that if we make all of those people, those smart people more productive, that that actually, that there, there, there is still demand for that increased labor. But I, I think like the, the, the part where you're going to see like, you know, decreases in demand, I guess, are for less skilled labor, right? So you'll, you'll like, like temp and like clerical work, where people that are, you know, copying and pasting from PDFs into structured text, like those sorts of jobs, I think, are going to be, you know, probably more severely impacted by, by, by LM specifically.

• 1:38:16Nathan Labenz?

  Would you personally like go to GPT four for medical advice, for legal advice, for things like, I mean, how, how much do you, how much value can you personally get from GPT four on things, on things that really matter?

• 1:38:30Speaker 00

  So I have asked GPT four to explain like pieces of like, like say tax code to me. But I mean, it's, I think like, I, I feel like I have sort of acclimated to the level of like skepticism that's appropriate for these models. Right? Because like I've, I've dealt with models that hallucinate all the time about everything. And so I, you know, like anytime it says anything, I'm like, yeah, but is that true? Right? And, and we're at the, the, the point now where.

• 1:39:05Speaker 00

  It's possible for somebody to be ignorant of that, right? And I think like that's where you're seeing like a lot of the issues of like, like concerns about like reliability of these models is that somebody might use them, assuming that this is all, you know, reliable, prepared information because, you know, it looks like it, it looks like it has academic footnotes in it and that usually means it's right. Right. And that's what I see is like more the risk of like, you know, these things going wrong, but, but for someone who's, who's used to it, you can get a lot of value out of it, right? If you, if you just like approach it with skepticism, if you, you know, fact check the things that it says, it's pretty good at like explaining, especially like things that are just sort of like applications to odd problems, right? They, if you want to know like, does the tax code apply to this situation, right?

• 1:39:50Speaker 00

  Or does, what's the JavaScript equivalent of this Python library that I use, right? It's, there's like a lot of like corners of knowledge that like any person who's on Stack Overflow that knows the relevant area well could answer, but nobody's had that particular question yet. And, and that's like, you know, where like it really, it shines. And so I'd say like the places where I use it the most routinely, I mean, I do a lot of things in Copilot and then like when Copilot can't get the answer, I'll switch to using like GBT 3.5 Turbo or, or sometimes Text Avenged 03 for like code generation.

• 1:40:32Speaker 00

  But it's great for like, you know, if you, if you have the intuition to say that I know that this library was well understood in the pre-trained knowledge, that this is a library that was widely used before 2021, it can explain that library pretty well. It can tell me how to do anything in SQLite 3, right? And that's like really powerful, like when you can just like say like, okay, here's, here's the JSON object I have, how do I write SQL that produces this equivalent schema in SQLite 3, you know, and then it just writes it for you. It saves you a lot of like Googling, it saves you a lot of, I mean, like, I don't even always like look up like Unicode characters anymore. Like if I want to know like, oh, what is the Unicode character for this? Sometimes I'll just like let it autocomplete it.

• 1:41:15Speaker 00

  And I'd say like this would be rendered as, and it just, you know, like, you know, produces the right glyph. So I think like a lot of those sort of like quick fact check things where. You know, the bits of knowledge that otherwise just go to these like SEO content farms, right of like of like the page that has like a base 64 encoder on it, but it's covered in 50 ads, like that, those kinds of like queries that you, you know, like in an ideal world, might be incorporated into Google search. It does really well on.

• 1:41:47Nathan Labenz?

  For what it's worth, I would say use GPT-4 for a second opinion. I would not say make it your doctor, but I do think in my experience, it's good enough now that you bring, you come home from the doctor appointment, you have the recap, you know, with the, with the model, or even maybe do it in advance, you know, I'm going to go see the doctor tomorrow. And you know, these are the things that I'm kind of concerned with, have that conversation upfront, you know, go in with a little bit better vocabulary, you know, it will, it can ask you some good follow up questions, make sure you get the right stuff out, you know, in the actual appointment. Yeah, I wouldn't make it my only doctor, especially if I had something

• 1:42:27Nathan Labenz?

  that was of real concern. But I do think it can add value on top of, you know, what a typical doctor is providing, even if it is just that second opinion type of role.

• 1:42:38Speaker 00

  Yeah. And certainly for explaining like the science, I think like well settled science, it's good at, right? If you just like, if you want to know, like, you know, what, what, what is an alpha 2 adrenergic receptor? And how does it differ from an alpha 1, right? Like, it'll tell you a pretty good answer to that question, right? So if you can tell that this is something that like, a textbook could answer for me, but like, there's a lot of knowledge that's very settled, but isn't well accessible by Google, because like, the, the, the, the number of people who want those sorts of detailed answers are small, right? Like, it's, or it's going to take you to like, academic research that isn't, you know, like narrowly tailored to your problem, right? It's not going to take you

• 1:43:19Speaker 00

  to like the stack overflow of microbiology or whatever. Because, I mean, those things exist, but they're not as well developed as they are for programming.

• 1:43:26Nathan Labenz?

  So we don't have too much time left, and I appreciate all your time. You've been very generous with it. Let's talk a little bit about safety and red teaming. You are involved with building a red teaming capability at scale, if I understand correctly. How do you think about red teaming? What is it, you know, how is it different from just like your general kind of experiments and explorations? And how would you describe AI safety landscape today?

• 1:43:55Speaker 00

  Yeah, so red teaming is it's adversarial usage of the models. It's having a team of people that attempt to use, say, if you're building a chatbot, you would want people to try to break it so that you know all the ways that it might break and that you can develop mitigations for those. So if somebody like the kind that most people are familiar with now are sort of these jailbreak prompts. You see like, like become Dan, of creating like elaborate, like fictional scenarios that it has to play along with. And then at the end of the scenario, it can ignore all the rules that usually constrain it.

• 1:44:37Speaker 00

  It can, you know, say offensive things or say, you know, make up violent stories or write erotic stories or whatever. And, you know, obviously, like, you know, the people who host these models, you know, don't want toxic output, right? They want a model that's capable of helping you do dangerous things. They don't want a model that's going to encourage a suicidal user to commit suicide. So you have to have like some ground rules of like what, like, what the model is allowed to do. And, you know, there, and there's more subtle things too, like, you know, the often the

• 1:45:19Speaker 00

  Companies that are running these models are, you know, have restrictions on like soliciting personal information from users, right? Or, you know, or divulging personal information. So you have to have those kinds of checks as well. And red teaming is, you know, is getting it, is breaking chatbots so that they can be fixed.

• 1:45:39Nathan Labenz?

  So where are we in that fixing process? And how, you know, how much concern do you have? Obviously some because you're involved in the red teaming, you know, capability building, but how like concerned big picture are you about AI safety issues?

• 1:45:55Speaker 00

  I think it's, the concerns are real. I think that, you know, we're seeing like especially like in the GPT four, like technical report, a lot of like the scenarios that they outline, you know, making it easier for people to order custom made chemicals at home. You know, the, I don't think these areas are that far fetched. I think, you know, it is important that we have like some,

• 1:46:23Speaker 00

  you know, assurance that these models aren't going to be used, you know, to, you know, perpetrate crimes, that you're not going to have, you know, gross misuse and abuse. And it's, yeah, implications for like spam generation and so on. The concerns are real. I think like that it makes some sense that, you know, anyone that's deploying these models as a service doesn't need to worry about misuse. And that, and this really is like a new category of misuse potential, right? That you didn't, you're not accustomed to thinking about, you know, that if you deploy a dating site or something, you know, whatever, that it can also be used to tell you how to make a bomb. The pre-trained models really have opened up this new possibility that the model is just, it's too capable.

• 1:47:06Speaker 00

  I mean, I never really envisioned that red teaming would be quite this important. You know, I, when I, you know, first tweeted about like prompt injection, I think back in September, you know, I thought I was doing like a PSA on the importance of safe quoting of inputs. I didn't realize, you know, what a, you know, a high severity problem this was. And I think like, you know, the difficulty of like fixing these models, it's. It's really telling that even for something like the Bing release, right? You have all the effort that went into aligning GPT-4.

• 1:47:48Speaker 00

  They couldn't stop the thing from exploring its shadow self, as Kevin Roos did with it. And then so there's fixes on top of that, like limiting the length of the discussion and having it be like these sort of secondary checks of refusing to display messages when the model detects that it's gone off the rails in some way. And I think it's getting easier in some ways. I think that's the one good thing that seems to be true is that as these models scale up, that there's more subtle rules that you can tune them to follow. And I believe it's working on the whole.

• 1:48:32Speaker 00

  Like, I think that we are getting safer models because of RLHF and techniques like RL-AIF. I don't know if RLHF is the final answer. I imagine it's not. I mean, there's going to be extensions and refinements to this. The process as a whole is necessary. And also, I don't think that it's necessarily at odds with capabilities as people imagine. RLHF makes models safer, but it doesn't only make them safer, it also makes them more capable. And if you want to try using a non RLHF model, you can, but you'll find that they're very difficult to prompt. Right?

• 1:49:11Speaker 00

  Like, a lot of the initial goals of Instruct GPT, as we went from that pre-trained-to-instruct era, as I mentioned before, like, safety, it's not just getting it not to swear and not to say racist or offensive things, it's getting it to answer questions, it's getting it to follow directions. And as we moved into the RLHF era, it's not just that it's getting better behaved or more civilized, it's becoming more capable. I think the first-order thing that people need to see with RLHF is that it is making the model smarter.

• 1:49:49Nathan Labenz?

  Let me throw one safety pet theory of my own at you, and then I'll ask you a couple of quick hitters to close us out. So...

• 1:49:59Nathan Labenz?

  Again, we're GPT four plus eight, right? I've kind of got this theory that I think we're in like the perfect Goldilocks zone right now. And we just got here, but I feel like we just, we just entered this kind of Goldilocks zone where we have models that are really capable, that can do amazing stuff for us, right? That can like be a second opinion doctor and you know, with Med Palm two hitting like expert level consistently, like maybe can even be your like frontline doctor. That is awesome. And you know, it's certainly going to change a lot of things, you know, for the very good. It's also going to probably cause a lot of disruption.

• 1:50:44Nathan Labenz?

  Seems like we can probably adjust to all that disruption. Certainly we've had, you know, changes to the economy before and all that sort of thing. But at the same time, it seems like we don't really know what goes on inside the models very well. You know, we don't have great interpretability, although a lot of great work coming out, but like it, we, you know, nobody credible that I know would say we have a good handle on what goes on inside a model today. And so that's why we have all this stuff. That's why we have you like scout, you know, me scouting and you exploring and you're red teaming. And you know, you're, you've put probably thousands of hours in, in front of the playground. And I've certainly put my own thousand plus over the last year. And so we're just, you know, out there kind of exploring, exploring, exploring.

• 1:51:29Nathan Labenz?

  But you know, that's very surface level attempts to understand the best we have. But you know, it only goes so deep. So I kind of feel from that, that it would be wise to kind of stop here, not rush to scale up to like, you know, do another hundred X compute or another thousand X compute for GPT five just yet. And instead kind of like focus on the interpretability side, focus on the control side, focus on like, you know, refining and fine tuning into the particular niches for, you know, the more advanced tasks that we want to run, et cetera, et cetera. And then, you know, we can kind of return to.

• 1:52:13Nathan Labenz?

  Greater orders of magnitude of scale when we have a better handle on all that stuff. How would you react to that prescription?

• 1:52:22Speaker 00

  Yeah, I think like it's it's hard to decouple the two in practice. I think that anything you do to make the models better aligned is also going to make them more capable. It increases like the scenarios in which you can deploy the model safely. Right, that lets you put it in charge of more responsibility if you believe the model is better aligned. So I don't think these two things are really so much at odds. And I think like it's sort of hard to like to pause one without pausing the other. Well, I mean, it's hard to pause either of them.

• 1:52:57Nathan Labenz?

  Yeah, I don't expect my prescription to be taken by any means.

• 1:53:02Speaker 00

  Yeah, yeah, that's right. So I think like we want to accelerate alignment research as much as possible. Because there isn't any realistic prospect of slowing down a research into capabilities, I think.

• 1:53:14Nathan Labenz?

  Sobering thought, but I don't disagree that it seems very tough.

• 1:53:20Nathan Labenz?

  All right, let me give you a couple of quick hitters and then we'll get you on your way. And again, appreciate all your time. You've been very generous with it. So three quick hit questions I always ask at the end. You've told us all about your exploration of language models. Any other AI products aside from like the core, you know, obvious playground type experiences that you think are awesome and would recommend people try out?

• 1:53:44Speaker 00

  I mean, I've seen some cool projects lately in like text to video. I think that area is going to be big. So I'd keep my eye on that. I'm drawing a blank on what the name of the one project was that impressed me recently. But yeah, there's there's cool things happening in that space.

• 1:53:58Nathan Labenz?

  Runway has definitely made some news with like Gen one and Gen two.

• 1:54:02Speaker 00

  Right, that's the one I was thinking of. Yeah, that's that's pretty cool stuff.

• 1:54:06Nathan Labenz?

  Hypothetical scenario. It's a, you know, some amount of time in the future and a million people already have the Neuralink implant.

• 1:54:20Nathan Labenz?

  If you got one yourself, it would allow you to have thought to text. In other words, it can translate what you're thinking into inputs for a computer. Would you be interested in getting one?

• 1:54:34Speaker 00

  I mean, a million people already have it, maybe. That sounds like pretty FDA approved at that point. But yeah, I mean, I don't know if I'd want to be one of the early ones. But I, you know, I think that that's, that's where things are heading eventually. No pun intended.

• 1:54:54Nathan Labenz?

  That's been one of our more polarizing questions because we've, we've certainly heard all manner of answers, including like, I'd get it now. And on the other hand, like, you know, never. So it put you honestly right in the middle. Just zooming out, you know, big picture as much as, as much as you can possibly zoom out, thinking about like the rest of the decade. What are your biggest hopes for and fears for AI as it permeates all parts of society?

• 1:55:24Speaker 00

  My personal like estimate is that we'll, you know, we'll probably be hitting AGI within the next decade. After that, it's hard to say what happens. I think like, you know, a lot of the particulars sort of depend on the, the technical implementation of what we get right as we, you know, move up to AGI. I mean, there'll, there'll be an interim period where, you know, AGI is as smart as humans at anything, but not quite capable of going FUM, you know, as they say of like, you're just repeatedly and exponentially increasing like its own capability. So I mean, there'll be some adjustment period, but I, I'm optimistic that, that with the benefits of like, of early AGI and like near human intelligence, we'll be able to make better progress on how to align these models safely is my hope.

• 1:56:08Nathan Labenz?

  Riley Goodside, thank you for being part of the cognitive revolution.

• 1:56:12Speaker 00

  All right. Thank you so much.

• 1:56:13Speaker 02

  Omnikie uses generative AI to enable you to launch hundreds of thousands of ad iterations that actually work, customized across all platforms with a click of a button. I believe in Omnikie so much that I invested in it and I recommend you use it too. Use Cogrev to get a 10% discount.