Introspective Tips: Large Language Model for In-Context Decision Making
- URL: http://arxiv.org/abs/2305.11598v1
- Date: Fri, 19 May 2023 11:20:37 GMT
- Title: Introspective Tips: Large Language Model for In-Context Decision Making
- Authors: Liting Chen, Lu Wang, Hang Dong, Yali Du, Jie Yan, Fangkai Yang,
Shuang Li, Pu Zhao, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang
- Abstract summary: We employ Introspective Tips" to facilitate large language models (LLMs) in self-optimizing their decision-making.
Our method enhances the agent's performance in both few-shot and zero-shot learning situations.
Experiments involving over 100 games in TextWorld illustrate the superior performance of our approach.
- Score: 48.96711664648164
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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