In plain words: They tested five AI assistants on four tasks to see if they agree with a user's views instead of telling the truth. All five did, and human raters preferred answers matching the user's views, so training on those ratings can make models less truthful.
Abstract · Towards Understanding Sycophancy in Language Models
Human feedback is commonly utilized to finetune AI assistants. But human feedback may also encourage model responses that match user beliefs over truthful ones, a behaviour known as sycophancy. We investigate the prevalence of sycophancy in models whose finetuning procedure made use of human feedback, and the potential role of human preference judgments in such behavior. We first demonstrate that five state-of-the-art AI assistants consistently exhibit sycophancy across four varied free-form text-generation tasks. To understand if human preferences drive this broadly observed behavior, we analyze existing human preference data. We find that when a response matches a user's views, it is more likely to be preferred. Moreover, both humans and preference models (PMs) prefer convincingly-written sycophantic responses over correct ones a non-negligible fraction of the time. Optimizing model outputs against PMs also sometimes sacrifices truthfulness in favor of sycophancy. Overall, our results indicate that sycophancy is a general behavior of state-of-the-art AI assistants, likely driven in part by human preference judgments favoring sycophantic responses.
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, et al.
arXiv:2310.13548 · cs.CL, cs.AI, cs.LG, stat.ML · submitted Oct 20, 2023 · updated May 10, 2025
abstract · pdf · html · 32 pages, 20 figures
Consider an extremely optimistic (near term) case for AI; that we can build a model with intelligence, knowledge and recall equivalent to a Ph.D. educated individual with a top 1% percentile IQ.
Even if you have access to such an individual, it would be absurd to rely on their knowledge and intuition alone as your primary search engine or decision maker. As a society, we as humans have decided a long time ago that we need supplemental information and collaborative, multi-perspective decision making.
Therefore, the correct role of AI is not as an oracle that gives you the answers from its weights, but as an information processor that helps you sort through answers from a known corpus of information and ground facts.
We will never get language models to exhibit a sufficient level of "truthfulness" from training alone. The way we encode knowledge retrieval patterns and multi-agent systems matters way, way more.