Researchers propose NashEval for context-dependent AI agent evaluation
A new paper on arXiv introduces NashEval, a framework for evaluating AI agents based on context-dependent preferences. Traditional methods like Bradley-Terry models assume transitive rankings, but human judgments often vary by context—such as prompts, tasks, or user groups. The authors model evaluation as a game where two players compete to select agent distributions that gain collective…
Key points
- NashEval frames agent evaluation as a game-theoretic equilibrium problem to handle heterogeneous human preferences
- Debiased payoff estimation and orthogonal loss learning avoid bias from sparse offline feedback
- Experiments demonstrate improved robustness and consistent top-agent identification across contexts
NashEval addresses bias in offline feedback by debiasing payoff estimates and learning equilibrium mappings without solving separate games per context. Experiments show it improves robustness and consistently identifies top agents across contexts. The paper argues that errors in payoff estimation only minimally affect equilibrium risk, making it a scalable approach for real-world deployment.
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