June 6, 2024, 4:52 a.m. | Shiguang Guo, Ziliang Deng, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.02903v1 Announce Type: new
Abstract: The emergence of large language models (LLMs) has increasingly drawn attention to the use of LLMs for human-like planning. Existing work on LLM-based planning either focuses on leveraging the inherent language generation capabilities of LLMs to produce free-style plans, or employs reinforcement learning approaches to learn decision-making for a limited set of actions within restricted environments. However, both approaches exhibit significant discrepancies from the open and executable requirements in real-world planning. In this paper, we …

abstract arxiv attention benchmark capabilities challenges construction cs.cl decision emergence free human human-like language language generation language models large language large language models learn llm llms making planning reinforcement reinforcement learning style type work

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