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Training language models to follow instructions with human feedback

Summary

Making language models larger does not by itself make them more helpful: they make up facts, generate toxic text, or disregard the user's request. The authors from OpenAI propose aligning GPT-3 with human intent by fine-tuning on human feedback. First, 40 hired labelers wrote demonstration responses to prompts from the OpenAI API, mostly in English, as well as to their own prompts. The model was fine-tuned on these examples with supervised learning (SFT). The labelers then ranked several candidate responses from best to worst. A reward model (RM) was trained on these comparisons, and the model was then fine-tuned against it with the PPO algorithm, i.e., by the RLHF method. The resulting models were named InstructGPT. Evaluators preferred the responses of the 1.3B-parameter InstructGPT to those of the 175B GPT-3, even though it is roughly one hundred times smaller. InstructGPT is also more truthful and slightly less toxic. The work showed that human feedback can yield more than simply scaling up the model, and it laid the methodological foundation for modern instruction-following assistants.

Key points

  • The models of three sizes (1.3B, 6B, 175B) use the GPT-3 architecture. Evaluators preferred the responses of the 175B InstructGPT to those of the standard 175B GPT-3 in 85 ± 3% of cases. Against GPT-3 with a few-shot prompt, this figure was 71 ± 4%.
  • On TruthfulQA, InstructGPT gives truthful and informative answers about twice as often as GPT-3. On closed-domain tasks such as summarization, the model fabricates information absent from the input text less often: 21% of cases versus 41%.
  • On RealToxicityPrompts, there are about 25% fewer toxic outputs when the model is prompted to respond respectfully. On Winogender and CrowS-Pairs, there is no noticeable reduction in bias.
  • The cost of alignment (the "alignment tax") is a drop in performance on SQuAD, DROP, HellaSwag, and WMT 2015 fr→en. The PPO-ptx variant, in which pretraining data are mixed into training, largely eliminates these losses without lowering labeler ratings.
  • GPT-3 fine-tuned on the FLAN and T0++ datasets falls noticeably short of InstructGPT: its win rate against the baseline SFT model is 29.8% and 26.8%, respectively, whereas for InstructGPT it is 73.4%. Labelers whose data were not used in training prefer InstructGPT about as often as those whose data were used.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: AI & machine learning

Topics: Language models & pretraining

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2203.02155 · arXiv 2203.02155

@misc{ouyang2022training,
  title = {{Training language models to follow instructions with human feedback}},
  author = {Ouyang, Long and Wu, Jeff and Jiang, Xu and Almeida, Diogo and Wainwright, Carroll L. and Mishkin, Pamela and Zhang, Chong and Agarwal, Sandhini and Slama, Katarina and Ray, Alex and Schulman, John and Hilton, Jacob and Kelton, Fraser and Miller, Luke and Simens, Maddie and Askell, Amanda and Welinder, Peter and Christiano, Paul and Leike, Jan and Lowe, Ryan},
  year = {2022},
  doi = {10.48550/arxiv.2203.02155},
  eprint = {2203.02155},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2203.02155}
}

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