Introspective Tips: Large Language Model for In-Context Decision Making
Authors: name: Liting Chen; name: Lu Wang; name: Hang Dong; name: Yali Du; name: Jie Yan; name: Fangkai Yang; name: Shuang Li; name: Pu Zhao; name: Si Qin; name: Saravan Rajmohan; name: Qingwei Lin; name: Dongmei Zhang
Published: 2023-05-19 11:20:37

Abstract: The emergence of large language models (LLMs) has substantially influenced
natural language processing, demonstrating exceptional results across various
tasks. In this study, we employ ``Introspective Tips" to facilitate LLMs in
self-optimizing their decision-making. By introspectively examining
trajectories, LLM refines its policy by generating succinct and valuable tips.
Our method enhances the agent's performance in both few-shot and zero-shot
learning situations by considering three essential scenarios: learning from the
agent's past experiences, integrating expert demonstrations, and generalizing
across diverse games. Importantly, we accomplish these improvements without
fine-tuning the LLM parameters; rather, we adjust the prompt to generalize
insights from the three aforementioned situations. Our framework not only
supports but also emphasizes the advantage of employing LLM in in-contxt
decision-making. Experiments involving over 100 games in TextWorld illustrate
the superior performance of our approach.