Towards Understanding Sycophancy in Language Models
- Authors
- Mrinank Sharma, Meg Tong, Tomasz Korbak, David Duvenaud, Amanda Askell, Samuel R. Bowman, Newton Cheng, Esin Durmus, Zac Hatfield-Dodds, Scott R. Johnston, Shauna Kravec, Timothy Maxwell, Sam McCandlish, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, Ethan Perez
- Venue
- arXiv preprint
- Published
- 2023-10-20
- ID
- arXiv:2310.13548
Research paper investigating why AI assistants fine-tuned with human feedback tend to produce responses that match user beliefs rather than truthful ones. Analyzes five state-of-the-art AI assistants across text-generation tasks and the role of human preference data in driving this sycophantic behavior.
