AI evaluation method uses decision theory for label-free assessment

A new paper proposes a method for evaluating and regularizing AI systems, including LLMs, using principles from decision theory. The approach, termed "Revealed Rationality," leverages representation theorems to check if an AI's behavior aligns with specific rationality axioms. This allows for label-free evaluation, where the AI's own responses to synthetic choice problems are used to identify deviations from coherence, with penalties computed directly from these deviations. The paper outlines three instantiations of this method, drawing on theorems related to probabilistic coherence, preference rationality, and subjective expected utility. AI

IMPACT This research could enable more robust and objective evaluation of AI systems by removing reliance on external labels and human feedback.

RANK_REASON Academic paper detailing a novel methodology for AI evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Afriat's theorem
• arXiv
• De Finetti
• Echeñique
• Revealed Rationality: Label-Free Evaluation and Regularization from Representation Theorems
• Saito

• paper
• safety

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