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Do LLM Agents Have Regrets? A Case Study in Online Learning and Games

Created by
  • Haebom

Author

Chanwoo Park, Xiangyu Liu, Asuman Ozdaglar, Kaiqing Zhang

Outline

This paper proposes a study measuring regret using benchmarks from online learning and game theory to quantitatively evaluate the decision-making ability of LLM-based autonomous agents. In particular, we analyze the performance of LLM agents in a multi-agent environment where they interact with each other. We empirically investigate the no-regret behavior of LLM, provide theoretical insights, and identify cases where advanced LLMs, such as GPT-4, fail to achieve no-regret. Furthermore, we propose a novel regret loss (regret-loss) that does not require (optimal) action labels, establish generalization guarantees, and demonstrate its potential for leading to no-regret learning algorithms. We verify the effectiveness of the proposed regret loss through experiments.

Takeaways, Limitations

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We present a novel framework for quantitatively evaluating the decision-making ability of LLM agents: measuring regret using online learning and game theory contributes to objectively evaluating the performance of LLM agents.
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Analyzing LLM Interactions in Multi-Agent Environments: Providing Important Insights for Understanding and Improving the Interactions of LLM Agents in Real-World Environments.
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Limitations Presentation of advanced LLMs such as GPT-4: Clarifies the limitations of existing LLMs and suggests the need for further research and improvement.
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A novel regret-loss proposal: Promotes no-regret behavior through unsupervised learning without labels, and provides generalization and optimization guarantees.
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Validation of the effectiveness of the proposed method through experiments: We demonstrate the practical effectiveness of the proposed method, especially confirming its improvement for "regrettable" cases.
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Margin:
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Assumptions about specific settings (e.g., supervised pre-training and the rationality of human decision-maker models) may influence theoretical insights.
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Further research is needed to explore the practical applicability and generalizability of regret loss.
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Further experiments are needed to determine how effectively the proposed method can be applied to other complex decision problems.
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