J
Jeffrey Wu
Researcher at OpenAI
Publications - Â 11
Citations - Â 16786
Jeffrey Wu is an academic researcher from OpenAI. The author has contributed to research in topics: Language model & Automatic summarization. The author has an hindex of 8, co-authored 9 publications receiving 3933 citations.
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Proceedings Article
Language Models are Few-Shot Learners
Tom B. Brown,Benjamin Mann,Nick Ryder,Melanie Subbiah,Jared Kaplan,Prafulla Dhariwal,Arvind Neelakantan,Pranav Shyam,Girish Sastry,Amanda Askell,Sandhini Agarwal,Ariel Herbert-Voss,Gretchen Krueger,Thomas Henighan,Rewon Child,Aditya Ramesh,Daniel M. Ziegler,Jeffrey Wu,Clemens Winter,Christopher Hesse,Mark Chen,Eric Sigler,Mateusz Litwin,Scott Gray,Benjamin Chess,Jack Clark,Christopher Berner,Samuel McCandlish,Alec Radford,Ilya Sutskever,Dario Amodei +30 more
TL;DR: GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic.
Posted Content
Language Models are Few-Shot Learners
Tom B. Brown,Benjamin Mann,Nick Ryder,Melanie Subbiah,Jared Kaplan,Prafulla Dhariwal,Arvind Neelakantan,Pranav Shyam,Girish Sastry,Amanda Askell,Sandhini Agarwal,Ariel Herbert-Voss,Gretchen Krueger,Thomas Henighan,Rewon Child,Aditya Ramesh,Daniel M. Ziegler,Jeffrey Wu,Clemens Winter,Christopher Hesse,Mark Chen,Eric Sigler,Mateusz Litwin,Scott Gray,Benjamin Chess,Jack Clark,Christopher Berner,Samuel McCandlish,Alec Radford,Ilya Sutskever,Dario Amodei +30 more
TL;DR: This article showed that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches.
Proceedings ArticleDOI
Training language models to follow instructions with human feedback
Long Ouyang,Jeffrey Wu,Xu Jiang,Diogo Almeida,Carroll L. Wainwright,Pamela Mishkin,Chong Zhang,Sandhini Agarwal,Katarina Slama,Alex Ray,John Schulman,Jacob Hilton,Fraser Kelton,Luke E. Miller,Maddie Simens,Amanda Askell,Peter Welinder,Paul F. Christiano,Jan Leike,Ryan Lowe +19 more
TL;DR: The results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent and showing improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets.
Posted Content
Scaling Laws for Neural Language Models
Jared Kaplan,Samuel McCandlish,Thomas Henighan,Tom B. Brown,Benjamin Chess,Rewon Child,Scott Gray,Alec Radford,Jeffrey Wu,Dario Amodei +9 more
TL;DR: Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence.
Proceedings Article
Generative Pretraining From Pixels
TL;DR: This work trains a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure, and finds that a GPT-2 scale model learns strong image representations as measured by linear probing, fine-tuning, and low-data classification.