Introspective Tips: Large Language Model for In-Context Decision Making
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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Liting Chen
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2023-07-31T01:47:22.608Z
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Liting Chen
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IantheChen
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Lu Wang
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Hang Dong
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Yali Du
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2023-05-23T06:39:32.516Z
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yali du
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YaliDU
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Jie Yan
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Fangkai Yang
extracted_pending
2024-07-22T03:18:50.161Z
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kaikai yang
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keanudicap
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Shuang Li
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Pu Zhao
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Si Qin
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Saravan Rajmohan
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Qingwei Lin
646ad3311556443f24b9351b
Dongmei Zhang
Introspective Tips: Large Language Model for In-Context Decision Making
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.
Introspective Tips improve LLM decision-making in few-shot and zero-shot learning without parameter fine-tuning by enhancing prompt design.
large language models introspective tips self-optimizing decision-making few-shot learning zero-shot learning agent's past experiences expert demonstrations prompt in-context decision-making TextWorld