Truthfulness Despite Weak Supervision: Evaluating and Training LLMs Using Peer Prediction

TL;DR

This paper introduces a peer prediction method for evaluating and training large language models using weak supervision, effectively promoting honesty and resisting deception without relying on ground truth labels, with theoretical and empirical validation.

Contribution

It applies game-theoretic peer prediction to LLM evaluation and training, providing a novel approach that improves truthfulness and robustness against deception in weak supervision scenarios.

Findings

Peer prediction enhances truthfulness in LLM training.

The method is effective even with minimal supervision signals.

Resistance to deception increases with larger size gaps between models.

Abstract

The evaluation and post-training of large language models (LLMs) rely on supervision, but strong supervision for difficult tasks is often unavailable, especially when evaluating frontier models. In such cases, models are demonstrated to exploit evaluations built on such imperfect supervision, leading to deceptive results. However, underutilized in LLM research, a wealth of mechanism design research focuses on game-theoretic incentive compatibility, i.e., eliciting honest and informative answers with weak supervision. Drawing from this literature, we introduce the peer prediction method for model evaluation and post-training. It rewards honest and informative answers over 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 empirical validation on models with up to 405B parameters. We show that training an 8B model with peer prediction-based reward recovers most of the drop in truthfulness due to prior malicious finetuning, even when the reward is produced by a 0.135B language model with no finetuning. On the evaluation front, 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 experts and participants widens, enabling reliable evaluation of strong models with weak supervision. In particular, LLM-as-a-Judge become worse than random guess when facing deceptive models 5-20x the judge's size, while peer prediction thrives when such gaps are large, including in cases with over 100x size difference.