Language Models Learn to Mislead Humans via RLHF

Jiaxin Wen, Ruiqi Zhong, Akbir Khan, Ethan Perez, Jacob Steinhardt, Minlie Huang, Samuel R. Bowman, He He, Shi Feng

Keywords: RLHF, reward hacking, human evaluation

TL;DR: We find LMs can unintendely learn to mislead real human evaluators on realistic tasks via RLHF.

Abstract:

Language models (LMs) can produce errors that are hard to detect for humans, especially when the task is complex.
RLHF, the most popular post-training method, may exacerbate this problem: to achieve higher rewards, LMs might get better at convincing humans that they are right even when they are wrong. We study this phenomenon under a standard RLHF pipeline, calling it ``U-Sophistry'' since it is \textbf{U}nintended by model developers. Specifically, we ask time-constrained (e.g., 3-10 minutes) human subjects to evaluate the correctness of model outputs and calculate humans' accuracy against gold labels. On a question-answering task (QuALITY) and programming task (APPS), RLHF makes LMs better at convincing our subjects but not at completing the task correctly. RLHF also makes the model harder to evaluate: our subjects' false positive rate increases by 24.1% on QuALITY and 18.3% on APPS.
Finally, we show that probing, a state-of-the-art approach for detecting \textbf{I}ntended Sophistry (e.g.~backdoored LMs), does not generalize to U-Sophistry. Our results highlight an important failure mode of RLHF and call for more research in assisting humans to align them.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 3124