Shibani Santurkar
@ShibaniSan
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🍓 excited!
We're releasing a preview of OpenAI o1—a new series of AI models designed to spend more time thinking before they respond. These models can reason through complex tasks and solve harder problems than previous models in science, coding, and math.
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💙💙💙💙💙💙💙
We have reached an agreement in principle for Sam Altman to return to OpenAI as CEO with a new initial board of Bret Taylor (Chair), Larry Summers, and Adam D'Angelo. We are collaborating to figure out the details. Thank you so much for your patience through this.
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🚢 🚢 🚢
ChatGPT with voice is now available to all free users. Download the app on your phone and tap the headphones icon to start a conversation. Sound on 🔊
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💛
I deeply regret my participation in the board's actions. I never intended to harm OpenAI. I love everything we've built together and I will do everything I can to reunite the company.
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❤️
i love the openai team so much
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Dear Embassy team, I am an Indian citizen studying in San Diego. I misplaced my passport while travelling from US to Greece via Canada. I am in contact with the consulate in Vancouver but desperate for help. @IndiainToronto
@IndiaPassportDC
@IndianDiplomacy
@DrSJaishankar
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We're launching ten $100,000 grants for building prototypes of a democratic process for steering AI. Our goal is to fund experimentation with methods for gathering nuanced feedback from everyone on how AI should behave. Apply by June 24, 2023:
openai.com
Our nonprofit organization, OpenAI, Inc., is launching a program to award ten $100,000 grants to fund experiments in setting up a democratic process for deciding what rules AI systems should follow,...
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Very cool work by @tatsu_hashimoto and colleagues: ask LLMs questions from Pew Surveys in order to measure whose opinions the model's outputs most closely reflects.
We know that language models (LMs) reflect opinions - from internet pre-training, to developers and crowdworkers, and even user feedback. But whose opinions actually appear in the outputs? We make LMs answer public opinion polls to find out: https://t.co/wv3F6TOnwe
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I would not say that LMs *have* opinions, but they certainly *reflect* opinions represented in their training data. OpinionsQA is an LM benchmark with no right or wrong answers. It's rather the *distribution* of answers (and divergence from humans) that's interesting to study.
We know that language models (LMs) reflect opinions - from internet pre-training, to developers and crowdworkers, and even user feedback. But whose opinions actually appear in the outputs? We make LMs answer public opinion polls to find out: https://t.co/wv3F6TOnwe
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We know that language models (LMs) reflect opinions - from internet pre-training, to developers and crowdworkers, and even user feedback. But whose opinions actually appear in the outputs? We make LMs answer public opinion polls to find out: https://t.co/wv3F6TOnwe
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As ML models/datasets get bigger + more opaque, we need a *scalable* way to ask: where in the *data* did a prediction come from? Presenting TRAK: data attribution with (significantly) better speed/efficacy tradeoffs: w/ @smsampark @kris_georgiev1 @andrew_ilyas @gpoleclerc 1/6
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Auto data selection is comparable to expert curated data for pretraining LMs! The leverage: n-gram overlap between pretrain and downstream predicts downstream acc well (r=0.89). But it's not the whole story - lots to uncover on the effect of pretrain data on downstream tasks.
Data selection typically involves filtering a large source of raw data towards some desired target distribution, whether it's high-quality/formal text (e.g., Wikipedia + books) for general-domain LMs like GPT-3 or domain-specific data for specialized LMs like Codex.
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I have 6 fantastic students and post-docs who are on the academic job market this year. Here is a short thread summarizing their work along with one representative paper:
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Our #NeurIPS2022 poster on in-context learning will be tomorrow (Thursday) at 4pm! Come talk to @shivamg_13 and me at poster #928 🔥
LLMs can do in-context learning, but are they "learning" new tasks or just retrieving ones seen during training? w/ @shivamg_13, @percyliang, & Greg Valiant we study a simpler Q: Can we train Transformers to learn simple function classes in-context? 🧵 https://t.co/3aQ0XWWPV9
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In August 2021, we launched CRFM with our report on foundation models. 15 months to the day, we now have launched HELM on the holistic evaluation of language models. Blog: https://t.co/ShKztgMMQ4 Website: https://t.co/N65Lb0Fj9N Paper: https://t.co/RiYXWLU1qV 1/n 🧵
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Language models are becoming the foundation of language technologies, but when do they work or don’t work? In a new CRFM paper, we propose Holistic Evaluation of Language Models (HELM), a framework to increase the transparency of LMs. Holistic evaluation includes three elements:
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LLMs can do in-context learning, but are they "learning" new tasks or just retrieving ones seen during training? w/ @shivamg_13, @percyliang, & Greg Valiant we study a simpler Q: Can we train Transformers to learn simple function classes in-context? 🧵 https://t.co/3aQ0XWWPV9
arxiv.org
In-context learning refers to the ability of a model to condition on a prompt sequence consisting of in-context examples (input-output pairs corresponding to some task) along with a new query...
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Based on our findings, we design simple interventions to improve CLIP’s ability to leverage web-scraped captions: by filtering them and using GPT-J to perform text data augmentations via paraphrasing.
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