How to elicit truth from models that may be mistaken❌ or deceptive😈? In our @CHAI_Berkeley paper @iclr_conf, we reward each model by how much its answer helps predict the others'. With weak supervision from a 0.14B LM, it enables anti-deception training on a 8B LM and overwhelmingly outperforms LLM-as-a-Judge. This technique, peer prediction, is adapted from the mechanism design literature, where it's known to be incentive-compatible, i.e., incentivizes honesty. The intuition is that, predicting mistakes/lies when you know the correct solution is relatively easy, while the opposite is asymmetrically hard. We are able to further show that, with a large and diverse pool of models, peer prediction incentivizes honesty even when the supervisor doesn't know the models' prior beliefs and motivations.