Training language models to follow instructions with human feedback
Ouyang et al., 2022, Training language models to follow instructions with human feedback (Advances in Neural Information Processing Systems 35 (NeurIPS 2022))
Subscriber deck — free during early access
1 cards in “3.5 Evaluation” · whole deck
3.5 Evaluation
- How was InstructGPT evaluated?Mainly by labeler preference on held-out API prompts from users not in training (win rate against 175B SFT, 1–7 Likert scores, and metadata such as following constraints or hallucinating), plus public data sets for truthfulness (TruthfulQA), toxicity (RealToxicityPrompts), bias (Winogender, CrowS-Pairs) and standard NLP tasks.
- Ouyang et al., 2022, Training language models to follow instructions with human feedback (Advances in Neural Information Processing Systems 35 (NeurIPS 2022)), §3.5
Source
- Long Ouyang, Jeff 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 Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike and Ryan Lowe. 2022. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems 35 (NeurIPS 2022). https://papers.nips.cc/paper_files/paper/2022/hash/b1efde53be364a73914f58805a001731-Abstract-Conference.html