Beyond Successful Play: Diagnosing Opponent Modeling in Large Language Models
We introduce a benchmark and diagnostic framework for repeated hide-and-seek against algorithmic opponents with explicitly defined recursive belief updates.
ProofPaper ↗
Key points
- Language-model agents are expected to adapt to others whose behavior changes through interaction.
- Their ground-truth beliefs and action probabilities let us distinguish adaptive play, action prediction, and belief tracking through live interaction, fixed-history evaluation, and controlled information interventions.
- With structured elicitation and behavioral supervised fine-tuning (SFT), we find improved play in several tested settings, but diagnostics reveal limitations in opponent prediction and belief tracking.
- We test whether supervising opponent action probabilities can address this predictive deficit in Qwen2.5-7B-Instruct.
Sources (1)
- [1]Beyond Successful Play: Diagnosing Opponent Modeling in Large Language ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 07:08 PM
We introduce a benchmark and diagnostic framework for repeated hide-and-seek against algorithmic opponents with explicitly defined recursive belief updates.
Language-model agents are expected to adapt to others whose behavior changes through interaction.
Extractive summary: sentences quoted from the sources.
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