Holiday Extras rolls out ChatGPT Enterprise across every team, boosting productivity by 500 hours weekly.

Superhuman introduces a new era of email with OpenAI.

GenAI RAG Revisited. Command-R. RAFT. RAT. RAG + Knowledge Graph. Devin AI Engineer. KPU (Knowledge Processing Unit). Open-Sora GenAI Vid. AutoDev. DeepMind SIMA. DeepSeek-VL. Amazon Chronos Models.

Posted by Mike Schaekermann, Research Scientist, Google Research, and Ivor Horn, Chief Health Equity Officer & Director, Google Core Health equity is a major societal concern worldwide with disparities having many causes. These sources include limitations in access to healthcare, differences in clinical treatment, and even fundamental differences in the diagnostic technology. In dermatology for example, skin cancer outcomes are worse for populations such as minorities, those with lower socioeconomic status, or individuals with limited healthcare access. While there is great promise in recent advances in machine learning (ML) and artificial intelligence (AI) to help improve healthcare, this transition from research to bedside must be accompanied by a careful understanding of whether and how they impact health equity. Health equity is defined by public health organizations as fairness of opportunity for everyone to be as healthy as possible. Importantly, equity may be different from equality. For example, people with greater barriers to improving their health may require more or different effort to experience this fair opportunity. Similarly, equity is not fairness as defined in the AI for healthcare literature. Whereas AI fairness often strives for equal performance of the AI technology across different patient populations, this does not center the goal of prioritizing performance with respect to pre-existing health disparities. Health equity considerations. An intervention (e.g., an ML-based tool, indicated in dark blue) promotes health equity if it helps reduce existing disparities in health outcomes (indicated in lighter blue). Health Equity Assessment of machine Learning performance (HEAL): a framework and dermatology AI model case study”, published in The Lancet eClinicalMedicine, we propose a methodology to quantitatively assess whether ML-based health technologies perform equitably. In other words, does the ML model perform well for those with the worst health outcomes for the condition(s) the model is meant to address? This goal anchors on the principle that health equity should prioritize and measure model performance with respect to disparate health outcomes, which may be due to a number of factors that include structural inequities (e.g., demographic, social, cultural, political, economic, environmental and geographic). The health equity framework (HEAL) Framework for Health Equity Assessment of machine Learning performance (HEAL). Our guiding principle is to avoid exacerbating health inequities, and these steps help us identify disparities and assess for inequitable model performance to move towards better outcomes for all. Case study on a dermatology model prior work. This example dermatology model was trained to classify 288 skin conditions using a development dataset of 29k cases. The input to the model consists of three photos of a skin concern along with demographic information and a brief structured medical history. The output consists of a ranked list of possible matching skin conditions. Using the HEAL framework, we evaluated this model by assessing whether it prioritized performance with respect to pre-existing health outcomes. The model was designed to predict possible dermatologic conditions (from a list of hundreds) based on photos of a skin concern and patient metadata. Evaluation of the model is done using a top-3 agreement metric, which quantifies how often the top 3 output conditions match the most likely condition as suggested by a dermatologist panel. The HEAL metric is computed via the anticorrelation of this top-3 agreement with health outcome rankings. We used a dataset of 5,420 teledermatology cases, enriched for diversity in age, sex and race/ethnicity, to retrospectively evaluate the model’s HEAL metric. The dataset consisted of “store-and-forward” cases from patients of 20 years or older from primary care providers in the USA and skin cancer clinics in Australia. Based on a review of the literature, we decided to explore race/ethnicity, sex and age as potential factors of inequity, and used sampling techniques to ensure that our evaluation dataset had sufficient representation of all race/ethnicity, sex and age groups. To quantify pre-existing health outcomes for each subgroup we relied on measurements from public databases endorsed by the World Health Organization, such as Years of Life Lost (YLLs) and Disability-Adjusted Life Years (DALYs; years of life lost plus years lived with disability). HEAL metric for all dermatologic conditions across race/ethnicity subpopulations, including health outcomes (YLLs per 100,000), model performance (top-3 agreement), and rankings for health outcomes and tool performance. (* Higher is better; measures the likelihood the model performs equitably with respect to the axes in this table.) HEAL metric for all dermatologic conditions across sexes, including health outcomes (DALYs per 100,000), model performance (top-3 agreement), and rankings for health outcomes and tool performance. (* As above.) HEAL metrics for all cancer and non-cancer dermatologic conditions across age groups, including health outcomes (DALYs per 100,000), model performance (top-3 agreement), and rankings for health outcomes and tool performance. (* As above.) Putting things in context Pareto condition (discussed further in the paper), which restricts model changes so that outcomes for each subpopulation are either unchanged or improved compared to the status quo, and performance does not worsen for any subpopulation. The HEAL framework, in its current form, assesses the likelihood that an ML-based model prioritizes performance for subpopulations with respect to pre-existing health disparities for specific subpopulations. This differs from the goal of understanding whether ML will reduce disparities in outcomes across subpopulations in reality. Specifically, modeling improvements in outcomes requires a causal understanding of steps in the care journey that happen both before and after use of any given model. Future research is needed to address this gap. Conclusion Acknowledgements The research described here is joint work across many teams at Google. We are grateful to all our co-authors: Terry Spitz, Malcolm Pyles, Heather Cole-Lewis, Ellery Wulczyn, Stephen R. Pfohl, Donald Martin, Jr., Ronnachai Jaroensri, Geoff Keeling, Yuan Liu, Stephanie Farquhar, Qinghan Xue, Jenna Lester, Cían Hughes, Patricia Strachan, Fraser Tan, Peggy Bui, Craig H. Mermel, Lily H. Peng, Yossi Matias, Greg S. Corrado, Dale R. Webster, Sunny Virmani, Christopher Semturs, Yun Liu, and Po-Hsuan Cameron Chen. We also thank Lauren Winer, Sami Lachgar, Ting-An Lin, Aaron Loh, Morgan Du, Jenny Rizk, Renee Wong, Ashley Carrick, Preeti Singh, Annisah Um'rani, Jessica Schrouff, Alexander Brown, and Anna Iurchenko for their support of this project.

[Hacker News discussion, LinkedIn discussion, Twitter thread] Update (Feb 2026): The full list of open source AI repos is hosted at Good AI List, updated daily. It’s balooned to 15K repos, and you can submit missing repos. You can also find some of them on my cool-llm-repos list on GitHub. Four years ago, I did an analysis of the open source ML ecosystem. Since then, the landscape has changed, so I revisited the topic. This time, I focused exclusively on the stack around foundation models. Data I searched GitHub using the keywords gpt, llm, and generative ai. If AI feels so overwhelming right now, it’s because it is. There are 118K results for gpt alone. To make my life easier, I limited my search to the repos with at least 500 stars. There were 590 results for llm, 531 for gpt, and 38 for generative ai. I also occasionally checked GitHub trending and social media for new repos. After MANY hours, I found 896 repos. Of these, 51 are tutorials (e.g. dair-ai/Prompt-Engineering-Guide) and aggregated lists (e.g. f/awesome-chatgpt-prompts). While these tutorials and lists are helpful, I’m more interested in software. I still include them in the final list, but the analysis is done with the 845 software repositories. It was a painful but rewarding process. It gave me a much better understanding of what people are working on, how incredibly collaborative the open source community is, and just how much China’s open source ecosystem diverges from the Western one. The New AI Stack I think of the AI stack as consisting of 3 layers: infrastructure, model development, and application development. Infrastructure At the bottom is the stack is infrastructure, which includes toolings for serving (vllm, NVIDIA’s Triton), compute management (skypilot), vector search and database (faiss, milvus, qdrant, lancedb), …. Model development This layer provides toolings for developing models, including frameworks for modeling & training (transformers, pytorch, DeepSpeed), inference optimization (ggml, openai/triton), dataset engineering, evaluation, ….. Anything that involves changing a model’s weights happens in this layer, including finetuning. Application development With readily available models, anyone can develop applications on top of them. This is the layer that has seen the most actions in the last 2 years and is still rapidly evolving. This layer is also known as AI engineering. Application development involves prompt engineering, RAG, AI interface, … Outside of these 3 layers, I also have two other categories: Model repos, which are created by companies and researchers to share the code associated with their models. Examples of repos in this category are CompVis/stable-diffusion, openai/whisper, and facebookresearch/llama. Applications built on top of existing models. The most popular types of applications are coding, workflow automation, information aggregation, … Note: In an older version of this post, Applications was included as another layer in the stack. AI stack over time I plotted the cumulative number of repos in each category month-over-month. There was an explosion of new toolings in 2023, after the introduction of Stable Diffusion and ChatGPT. The curve seems to flatten in September 2023 because of three potential reasons. I only include repos with at least 500 stars in my analysis, and it takes time for repos to gather these many stars. Most low-hanging fruits have been picked. What is left takes more effort to build, hence fewer people can build them. People have realized that it’s hard to be competitive in the generative AI space, so the excitement has calmed down. Anecdotally, in early 2023, all AI conversations I had with companies centered around gen AI, but the recent conversations are more grounded. Several even brought up scikit-learn. I’d like to revisit this in a few months to verify if it’s true. In 2023, the layers that saw the highest increases were the applications and application development layers. The infrastructure layer saw a little bit of growth, but it was far from the level of growth seen in other layers. Applications Not surprisingly, the most popular types of applications are coding, bots (e.g. role-playing, WhatsApp bots, Slack bots), and information aggregation (e.g. “let’s connect this to our Slack and ask it to summarize the messages each day”). AI engineering 2023 was the year of AI engineering. Since many of them are similar, it’s hard to categorize the tools. I currently put them into the following categories: prompt engineering, AI interface, Agent, and AI engineering (AIE) framework. Prompt engineering goes way beyond fiddling with prompts to cover things like constrained sampling (structured outputs), long-term memory management, prompt testing & evaluation, etc. AI interface provides an interface for your end users to interact with your AI application. This is the category I’m the most excited about. Some of the interfaces that are gaining popularity are: Web and desktop apps. Browser extensions that let users quickly query AI models while browsing. Bots via chat apps like Slack, Discord, WeChat, and WhatsApp. Plugins that let developers embed AI applications to applications like VSCode, Shopify, and Microsoft Offices. The plugin approach is common for AI applications that can use tools to complete complex tasks (agents). AIE framework is a catch-all term for all platforms that help you develop AI applications. Many of them are built around RAG, but many also provide other toolings such as monitoring, evaluation, etc. Agent is a weird category, as many agent toolings are just sophisticated prompt engineering with potentially constrained generation (e.g. the model can only output the predetermined action) and plugin integration (e.g. to let the agent use tools). Model development Pre-ChatGPT, the AI stack was dominated by model development. Model development’s biggest growth in 2023 came from increasing interest in inference optimization, evaluation, and parameter-efficient finetuning (which is grouped under Modeling & training). Inference optimization has always been important, but the scale of foundation models today makes it crucial for latency and cost. The core approaches for optimization remain the same (quantization, low-ranked factorization, pruning, distillation), but many new techniques have been developed especially for the transformer architecture and the new generation of hardware. For example, in 2020, 16-bit quantization was considered state-of-the-art. Today, we’re seeing 2-bit quantization and even lower than 2-bit. Similarly, evaluation has always been essential, but with many people today treating models as blackboxes, evaluation has become even more so. There are many new evaluation benchmarks and evaluation methods, such as comparative evaluation (see Chatbot Arena) and AI-as-a-judge. Infrastructure Infrastructure is about managing data, compute, and toolings for serving, monitoring, and other platform work. Despite all the changes that generative AI brought, the open source AI infrastructure layer remained more or less the same. This could also be because infrastructure products are typically not open sourced. The newest category in this layer is vector database with companies like Qdrant, Pinecone, and LanceDB. However, many argue this shouldn’t be a category at all. Vector search has been around for a long time. Instead of building new databases just for vector search, existing database companies like DataStax and Redis are bringing vector search into where the data already is. Open source AI developers Open source software, like many things, follows the long tail distribution. A handful of accounts control a large portion of the repos. One-person billion-dollar companies? 845 repos are hosted on 594 unique GitHub accounts. There are 20 accounts with at least 4 repos. These top 20 accounts host 195 of the repos, or 23% of all the repos on the list. These 195 repos have gained a total of 1,650,000 stars. On Github, an account can be either an organization or an individual. 19/20 of the top accounts are organizations. Of those, 3 belong to Google: google-research, google, tensorflow. The only individual account in these top 20 accounts is lucidrains. Among the top 20 accounts with the most number of stars (counting only gen AI repos), 4 are individual accounts: lucidrains (Phil Wang): who can implement state-of-the-art models insanely fast. ggerganov (Georgi Gerganov): an optimization god who comes from a physics background. Illyasviel (Lyumin Zhang): creator of Foocus and ControlNet who’s currently a Stanford PhD. xtekky: a full-stack developer who created gpt4free. Unsurprisingly, the lower we go in the stack, the harder it is for individuals to build. Software in the infrastructure layer is the least likely to be started and hosted by individual accounts, whereas more than half of the applications are hosted by individuals. Applications started by individuals, on average, have gained more stars than applications started by organizations. Several people have speculated that we’ll see many very valuable one-person companies (see Sam Altman’s interview and Reddit discussion). I think they might be right. 1 million commits Over 20,000 developers have contributed to these 845 repos. In total, they’ve made almost a million contributions! Among them, the 50 most active developers have made over 100,000 commits, averaging over 2,000 commits each. See the full list of the top 50 most active open source developers here. The growing China's open source ecosystem It’s been known for a long time that China’s AI ecosystem has diverged from the US (I also mentioned that in a 2020 blog post). At that time, I was under the impression that GitHub wasn’t widely used in China, and my view back then was perhaps colored by China’s 2013 ban on GitHub. However, this impression is no longer true. There are many, many popular AI repos on GitHub targeting Chinese audiences, such that their descriptions are written in Chinese. There are repos for models developed for Chinese or Chinese + English, such as Qwen, ChatGLM3, Chinese-LLaMA. While in the US, many research labs have moved away from the RNN architecture for language models, the RNN-based model family RWKV is still popular. There are also AI engineering tools providing ways to integrate AI models into products popular in China like WeChat, QQ, DingTalk, etc. Many popular prompt engineering tools also have mirrors in Chinese. Among the top 20 accounts on GitHub, 6 originated in China: THUDM: Knowledge Engineering Group (KEG) & Data Mining at Tsinghua University. OpenGVLab: General Vision team of Shanghai AI Laboratory OpenBMB: Open Lab for Big Model Base, founded by ModelBest & the NLP group at Tsinghua University. InternLM: from Shanghai AI Laboratory. OpenMMLab: from The Chinese University of Hong Kong. QwenLM: Alibaba’s AI lab, which publishes the Qwen model family. Live fast, die young One pattern that I saw last year is that many repos quickly gained a massive amount of eyeballs, then quickly died down. Some of my friends call this the “hype curve”. Out of these 845 repos with at least 500 GitHub stars, 158 repos (18.8%) haven’t gained any new stars in the last 24 hours, and 37 repos (4.5%) haven’t gained any new stars in the last week. Here are examples of the growth trajectory of two of such repos compared to the growth curve of two more sustained software. Even though these two examples shown here are no longer used, I think they were valuable in showing the community what was possible, and it was cool that the authors were able to get things out so fast. My personal favorite ideas So many cool ideas are being developed by the community. Here are some of my favorites. Batch inference optimization: FlexGen, llama.cpp Faster decoder with techniques such as Medusa, LookaheadDecoding Model merging: mergekit Constrained sampling: outlines, guidance, SGLang Seemingly niche tools that solve one problem really well, such as einops and safetensors. Conclusion Even though I included only 845 repos in my analysis, I went through several thousands of repos. I found this helpful for me to get a big-picture view of the seemingly overwhelming AI ecosystem. I hope the list is useful for you too. Please do let me know what repos I’m missing, and I’ll add them to the list!

We have partnered with international news organizations Le Monde and Prisa Media to bring French and Spanish news content to ChatGPT.

Healthify collaborates with OpenAI to improve millions of lives with sustainable weight loss.

AI Reasoning Like Humans. Self-Discover & Chain of Abstraction Reasoning. Claude 3 IQ Test. Neural Chess. FSDP + QLoRA. State of Competitive ML. Open Sora VideoGen.

New board members named and enhancements to the governance structure introduced

Dr. Sue Desmond-Hellmann, Nicole Seligman, Fidji Simo join; Sam Altman rejoins board

Lifespan uses GPT-4 to radically improve health literacy and patient outcomes.

Paradigm uses OpenAI’s API to improve patient access to clinical trials.

Match Group uses ChatGPT Enterprise to spark creativity and impact.

We are dedicated to the OpenAI mission and have pursued it every step of the way.

Announcing a new resource, the FM Dev Cheatsheet.

A challenge of building AI applications is choosing which model to use. What if we don’t have to? What if we can predict the best model for any prompt? Predictive human preference aims to predict which model users might prefer for a specific query. Human preference has emerged to be both the Northstar and a powerful tool for AI model development. Human preference guides post-training techniques including RLHF and DPO. Human preference is also used to rank AI models, as used by LMSYS’s Chatbot Arena. Chatbot Arena aims to determine which model is generally preferred. I wanted to see if it’s possible to predict which model is preferred for each query. One use case of predictive human preference is model routing. For example, if we know in advance that for a prompt, users will prefer Claude Instant’s response over GPT-4, and Claude Instant is cheaper/faster than GPT-4, we can route this prompt to Claude Instant. Model routing has the potential to increase response quality while reducing costs and latency. Another use case of predictive human preference is interpretability. Mapping out a model’s performance on different prompts can help us understand this model’s strengths and weaknesses. See section Experiment results for examples. Here’s what predictive human preference for different model pairs looks like for the prompt “What’s the best way to cluster text embeddings?”. The predictions were generated by my toy preference predictor. The bright yellow color for the (GPT-4, GPT-3.5-Turbo) cell means that my predictor thinks GPT-4’s response is very likely to be preferred to that of GPT-3.5-Turbo’s for this prompt. This post first discusses the correctness of Chatbot Arena, which will then be used as a baseline to evaluate the correctness of preference predictions. It then discusses how to build a preference predictor and the initial results. Ranking Models Using Human Preference Using preferential signals (comparisons) to rank models has grown in popularity in the last few years. Other than powering LMSYS’s Chatbot Arena, it’s also used by many model providers (Anthropic, Gemini, ChatGPT, etc.) to evaluate their models in production. Side note: Friends who have deployed this in production told me that most users don’t read both options and just randomly vote for one. This introduces a lot of noise. However, the signals from the small percentage of users who vote correctly can sometimes be sufficient to help determine which model is preferred, as long as there’s minimal bias in the random voting. How Preferential Ranking Works Preferential ranking works in two steps: Collect comparison data about user preference. Compute a model ranking from these comparisons. For each request, two or more models are selected to respond. An evaluator, which can be human or AI, picks the winner. The evaluator shouldn’t know which models are being judged. Each comparison is called a match. This process results in a series of comparisons. Match ID Prompt Model A Model B Winner 1 … Model 1 Model 2 Model 1 2 … Model 3 Model 1 Model 1 3 … Model 1 Model 4 Model 4 ... ... ... ... ... From these comparisons, we need to compute the rankings of all models. The two most common ranking algorithms are Elo (from chess) and TrueSkill (from video games). While Chatbot Arena refers to their model scores “Elo scores”, they actually don’t use Elo. In December 2023, they switched to Bradley-Terry but scaled the resulting scores to make them look Elo-like (see their notebook). Given a history of match outcomes, the Bradley-Terry algorithm finds the model scores that maximize the likelihood of these match outcomes, turning model scoring into a maximum likelihood estimation problem. The input, for each training example, is the models that participate in the match. The output is the outcome of the match. Assuming there’s no draw, the outcome of a match is either 0 (a wins) or 1 (b wins). Correctness of Chatbot Arena Ranking Given the same match outcomes, different ranking algorithms can produce different rankings. For example, the ranking computed by Elo might differ from the ranking computed by Bradley-Terry. How do we know that a ranking is correct? At its core, model ranking is a predictive problem. We compute a ranking from historical match outcomes and use it to predict future match outcomes. The quality of a ranking is determined by how accurately it can predict future match outcomes. Let’s say we have a match between model A and model B. If model A has a higher score, meaning that the ranking algorithm predicts that A wins. If users indeed prefer the higher-ranking model, the ranking algorithm makes a correct prediction. Eval data To compute the accuracy of Chatbot Arena ranking, I used their data published in July 2023, which consists of 33K crowd-sourced comparisons for matches among 20 models. I used this smaller dataset instead of their Jan 2024 dataset because this smaller dataset contains the prompt used for each match, which I need for predictive human preference. Benchmarking on this dataset allows me to compare my model with the Bradley-Terry algorithm later on. Here’s an example from their July 2023 dataset. prompt model_a model_b winner model_a's response model_b's response who was the last monarch of uk koala-13b vicuna-13b model_a The last monarch of the United Kingdom was Queen Elizabeth II, who reigned from 1952 to 2020. The current monarch of the United Kingdom is Queen Elizabeth II. She has been the monarch since 1952, and is the longest-reigning monarch in British history. For reference, the Bradley-Terry (BT) scores of the top 7 models in this dataset are as follows. GPT-4: 1189 Claude-v1: 1150 Claude-instant-v1: 1110 GPT-3.5-Turbo: 1104 WizardLM-13B: 1058 Vicuna-13b: 1040 Guanaco-33b: 1031 To create a test set, I randomly select 10% of the data (3300 examples). Each match has three possible outcomes: model_a wins, model_b wins, or tie. This can still be framed as a binary classification problem if we treat a tied match as two matches: one in which model_a wins and one in which model_b wins. Results I found that for all non-tie matches in my test set, the model with the higher Bradley-Terry score is preferred 74.1% of the time. This means that if we always predict the higher-ranked model as the winner for a match, we’d have an accuracy of 74.1%. Test data Output classes # samples BT's accuracy All matches model_a wins model_b wins tie 3,300 53.33% Non-tie matches model_a wins model_b wins 2,367 74.1% Non-tie matches involving GPT-4 model_a wins model_b wins 355 85.1% (always pick GPT-4 as winner) Back in July 2023, GPT-4 was considered the strongest model by a long shot (this was before Gemini, Mistral, Claude-v2). Did users always prefer GPT-4 to all other models? They didn’t. In 355 non-tie matches involving GPT-4, GPT-4 wins 85.1%. This means that even though GPT-4 is the best model overall, there are prompts for which other models can outperform GPT-4. If we can figure out which prompts these are, and which models work best for them, we can route these prompts to the best-performing models, improving the response quality. Predicting Human Preference For Each Prompt If a ranking algorithm is about figuring out which model is better overall, predictive human preference is about figuring out which model is better for each prompt. If we know in advance that for a particular prompt, GPT-3.5 works just as well as GPT-4, and GPT-3.5 is cheaper, we can route that prompt to GPT-3.5 instead. Or if we know that Mistral-7B works just as well as GPT-4 and Mistral-7B is faster, we can route our query to Mistral-7B instead. Model routing can also help with budget planning. Say, you only have enough budget to serve 50% of queries on the strongest model, and the rest to a weaker model, you want to make sure that you send to the weaker model only the queries that you’re confident it can do well on. Experiment setup I treat predictive human preference as a binary classification task. Given a match between 2 models, predict which one wins. If the probability of model_a winning is around 0.5, it can be considered a tie. If a Bradley-Terry model takes only (model_a, model_b) as the input, a preference predictor takes (prompt, model_a, model_b) as the input. The architecture of my preference predictor looks like this. The model encoder and preference predictor are neural networks that can be trained independently or together. I used DistilBERT as my prompt encoder. To train my model, I used 90% of LMSYS’s July 2023 dataset. I found that the predictor performed better using only non-tie matches (as opposed to using both tie and non-tie matches). I randomly flipped the order of models in a match 50% of the time. To evaluate my model, I used 10% of this data. This is the same test data used to evaluate the correctness of Chatbot Arena’s ranking above. Split All matches Non-tie matches Train 29,700 20,927 Test 3,300 2,367 Note: I should’ve made a separate validation set for hyperparameter tuning. However, given that I didn’t have a lot of data and this is only a proof of concept, I didn’t do it. (I’m also lazy.) The matches are among 20 models, corresponding to 190 model pairs. 20,927 comparisons mean that, on average, there are only 110 comparisons per model pair. Experiment results I evaluated my preference predictor under two settings: Using only model_a and model_b as the input. This is to see whether this predictor, using only model names, can make better predictions about match outcomes than Chatbot Arena scores. Using (prompt, model_a, model_b) as the input. This is to see whether including prompts helps improve match outcome prediction. I found that for all non-tie matches, my preference predictor can predict the match outcome accurately 75% of the time if not using prompts, and 76.2% of the time if using prompts. This suggests that human preference for models does change depending on the prompt. While the improvement doesn’t seem much, a 2.1% improvement can be significant at scale. Eval data # eval samples Chatbot Arena Preference predictor (without prompts) Preference predictor (with prompts) Non-tie matches 2,367 74.1% 75% 76.2% Non-tie matches involving GPT-4 355 85.1% 86.2% 87% Keep in mind that this predictor was trained with a small amount of crowd-sourced (e.g. noisy) data. The prompts crowdsourced are also simple. Among 33K prompts, 180 (0.55%) of them are “hello” and “hi”. These simple prompts are insufficient to distinguish strong models from weak ones. I suspect that with more/better data, the performance of this predictor can significantly improve. Domain-specific and query-specific leaderboards Recall that 20 models correspond to 190 model pairs. To visualize how the predictor captures human preference, for each evaluation prompt, I generated 190 different inputs, one for each model pair. I then visualized the 190 predictions for 190 model pairs in a 20 x 20 grid, as shown below for the prompt “Derive the elastic wave equation.” I only included 9 models in the plot to make it readable. The diagonal values refer to comparing a model to itself, so the predicted preference should be 0.5. Given the predicted preference for all model pairs for a prompt, I used a Bradley-Terry model (the same ranking algorithm that LMSYS uses) to create a leaderboard for this prompt. I used the same scaling that LMSYS uses to make the scores look Elo-like. Here’s the ranking of the 9 models shown above for the query “Derive the elastic wave equation.” This also means that with this preference predictor, we can create a leaderboard for any arbitrary subset of data. We can have a leaderboard specific to any domain. Model ranking for the prompt"Derive the elastic wave equation." gpt-4 1214 claude-v1 1162 gpt-3.5-turbo 1104 claude-instant-v1 1110 guanaco-33b 1023 vicuna-13b 1007 vicuna-7b 985 RWKV-4-Raven-14B 970 gpt4all-13b-snoozy 915 Despite being a toy predictor, the model seems to be able to capture different models’ performance patterns. One pattern is that for simple prompts, weak models can do (nearly) as well as strong models. For more challenging prompts, however, users are much more likely to prefer stronger models. Here’s a visualization of predicted human preference for an easy prompt (“hello, how are you?”) and a challenging prompt (“Explain why Planc length …”). Here are the model rankings for these two prompts. The score spread for the simple prompt is much less than the score spread for the challenging prompt. The models that are ranked differently for these two prompts are highlighted in red. The predictor is also the most confident that GPT-4 will be preferred for queries in Russian and queries that involve code writing. For example, the average predicted win rate for the following Russian query of GPT-4 against all other models is 91.55%. Notice that for this query, while claude-v1 is predicted to do well on this query, claude-instant-v1 is predicted to do poorly. Conclusion My primitive experiment suggests that predictive human preference is feasible using a surprisingly small amount of data. There are many potential use cases for predictive human preference – model routing and interpretability are just two of them. Predictive human reference is the first and the most important step in model routing (the other key step is routing strategy). With more and more models being developed, each with different capabilities and a cost structure, model routing has clear economic values. I’m aware of four groups (two in stealth) that are working on model routing. One startup is Martian, which announced its $9M seed round. LMSYS is also working on model routing, which I think is a natural progression from their work in comparative evaluation. While my experiment used human-annotated comparisons, LMSYS folks told me that due to the noisiness of crowd-sourced annotations and the costs of expert annotations, they’ve found that using GPT-4 to compare two responses works better. Depending on the complexity of the queries, generating 10,000 comparisons using GPT-4 would cost only $200 - 500, making this very affordable for companies that want to test it out. This is the most fun side project I’ve worked on in a while, so I’d love to talk more about it. For those interested, I’ll be hosting a casual 30-minute discussion on predictive human preference on Tuesday, Mar 5, 9.30am PST. Join our Discord or email me if you want an invite! Acknowledgment Thanks Luke Metz for helping me with the experiments and coercing me into using JAX. While JAX is super cool and makes a lot of things easy, it also caused some of the weirdest bugs I’ve ever seen. I’m glad I used it though. Thanks Han-chung Lee for feedback on the plots.

We explore large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios. We leverage a transformer architecture that operates on spacetime patches of video and image latent codes. Our largest model, Sora, is capable of generating a minute of high fidelity video. Our results suggest that scaling video generation models is a promising path towards building general purpose simulators of the physical world.

We’re testing the ability for ChatGPT to remember things you discuss to make future chats more helpful. You’re in control of ChatGPT’s memory.