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The emergence of large language models (LLMs) has substantially influencednatural language processing, demonstrating exceptional results across varioustasks. In this study, we employ ``Introspective Tips" to facilitate LLMs inself-optimizing their decision-making. By introspectively examiningtrajectories, LLM refines its policy by generating succinct and valuable tips.Our method enhances the agent's performance in both few-shot and zero-shotlearning situations by considering three essential scenarios: learning from theagent's past experiences, integrating expert demonstrations, and generalizingacross diverse games. Importantly, we accomplish these improvements withoutfine-tuning the LLM parameters; rather, we adjust the prompt to generalizeinsights from the three aforementioned situations. Our framework not onlysupports but also emphasizes the advantage of employing LLM in in-contxtdecision-making. Experiments involving over 100 games in TextWorld illustratethe superior performance of our approach.

Introspective Tips: Large Language Model for In-Context Decision Making