Go to ICLR 2025 Conference homepageTruthfulness Without Supervision: Model Evaluation Using Peer Prediction
Current evaluation methods for language models rely on supervision, but trusted supervision for difficult tasks is often unavailable, especially for superhuman models. In these cases, models have been demonstrated to exploit evaluation schemes built on such imperfect supervision, leading to deceptive evaluation results. However, underutilized in the context of model evaluation, a wealth of mechanism design research focuses on game-theoretic incentive compatibility - eliciting honest and informative answers without trusted supervision. Drawing from this literature, we introduce the peer prediction method for model evaluation. It tells apart honest and informative answers from deceptive and uninformative ones, using a metric based on mutual predictability and without requiring ground truth labels. We demonstrate the method's effectiveness and resistance to deception, with both theoretical guarantees and comprehensive empirical validation on up to 405B-parameter models. In contrast to LLM-as-a-Judge which requires strong and trusted judges, we discover an inverse scaling property in peer prediction, where, surprisingly, resistance to deception is strengthened as the capability gap between the jury and participants widens, enabling reliable evaluation of strong models without trusted supervision. In particular, LLM-as-a-Judge evaluations become worse than random guesses when facing deceptive models 5-20$\times$ its size, while peer prediction thrives when such gaps are large, including in cases with over 100$\times$ size difference. Looking forward, we view this work as a step towards game-theoretic resistance to model deception in alignment and evaluation.