4.3k citations · 9k across the 4 of their papers we have counts for
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Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang +17
Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic,…
Recursively Summarizing Books with Human Feedback
Jeff Wu, Long Ouyang, Daniel M. Ziegler +4
A major challenge for scaling machine learning is training models to perform tasks that are very difficult or time-consuming for humans to evaluate. We present progress on this pro…
Language Models are Few-Shot Learners
Tom B. Brown, Benjamin Mann, Nick Ryder +28
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typicall…
Fine-Tuning Language Models from Human Preferences
Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu +5
Reward learning enables the application of reinforcement learning (RL) to tasks where reward is defined by human judgment, building a model of reward by asking humans questions. Mo…
Release Strategies and the Social Impacts of Language Models
Irene Solaiman, Miles Brundage, Jack Clark +12
Large language models have a range of beneficial uses: they can assist in prose, poetry, and programming; analyze dataset biases; and more. However, their flexibility and generativ…