title: Introspective Tips: Large Language Model for In-Context Decision Making
summary: 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.
paper_authors._id: 646ad3311556443f24b93510
paper_authors.name: Liting Chen
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2023-07-31T01:47:22.608Z
paper_authors._id: 64c712732d07296c7e61a877
paper_authors.avatarUrl: /avatars/97b3cf0e180b1e48cbabc97897fe9732.svg
paper_authors.fullname: Liting Chen
paper_authors.type: user
paper_authors.user: IantheChen
paper_authors._id: 646ad3311556443f24b93511
paper_authors.name: Lu Wang
paper_authors._id: 646ad3311556443f24b93512
paper_authors.name: Hang Dong
paper_authors._id: 646ad3311556443f24b93513
paper_authors.name: Yali Du
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-23T06:39:32.516Z
paper_authors._id: 646b3c09628e5b50b2dd1174
paper_authors.avatarUrl: /avatars/f96a4d375ea44d10891f4b47b7d7986e.svg
paper_authors.fullname: yali du
paper_authors.type: user
paper_authors.user: YaliDU
paper_authors._id: 646ad3311556443f24b93514
paper_authors.name: Jie Yan
paper_authors._id: 646ad3311556443f24b93515
paper_authors.name: Fangkai Yang
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-07-22T03:18:50.161Z
paper_authors._id: 669dcf6200970c3b27aafa5d
paper_authors.avatarUrl: /avatars/bb9ed5ff86326fdaeb184c6b0e40f74f.svg
paper_authors.fullname: kaikai yang
paper_authors.type: user
paper_authors.user: keanudicap
paper_authors._id: 646ad3311556443f24b93516
paper_authors.name: Shuang Li
paper_authors._id: 646ad3311556443f24b93517
paper_authors.name: Pu Zhao
paper_authors._id: 646ad3311556443f24b93518
paper_authors.name: Si Qin
paper_authors._id: 646ad3311556443f24b93519
paper_authors.name: Saravan Rajmohan
paper_authors._id: 646ad3311556443f24b9351a
paper_authors.name: Qingwei Lin
paper_authors._id: 646ad3311556443f24b9351b
paper_authors.name: Dongmei Zhang
paper_title: Introspective Tips: Large Language Model for In-Context Decision Making
paper_summary: 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.
paper_ai_keywords: large language models (LLMs) Introspective Tips self-optimizing decision-making trajectories policy refinement few-shot learning zero-shot learning past experiences expert demonstrations prompt adjustment in-contxt decision-making TextWorld