Summary of Understanding the Planning Of Llm Agents: a Survey, by Xu Huang and Weiwen Liu and Xiaolong Chen and Xingmei Wang and Hao Wang and Defu Lian and Yasheng Wang and Ruiming Tang and Enhong Chen
Understanding the planning of LLM agents: A survey
by Xu Huang, Weiwen Liu, Xiaolong Chen, Xingmei Wang, Hao Wang, Defu Lian, Yasheng Wang, Ruiming Tang, Enhong Chen
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This survey provides a systematic view of Large Language Model (LLM) based autonomous agents’ planning capabilities. It covers recent works aiming to improve planning ability by leveraging LLMs as planning modules. The paper categorizes existing works into five directions: Task Decomposition, Plan Selection, External Module, Reflection, and Memory. Each direction is analyzed comprehensively, and the paper discusses future challenges for the field of research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous agents are getting smarter with the help of Large Language Models (LLMs). This paper looks at how LLMs can be used to plan better. It groups recent works into five areas: breaking down big tasks, choosing the right plan, using extra modules, reflecting on what happened, and remembering important things. The paper explains each area in detail and talks about what’s missing in this field of research. |
Keywords
* Artificial intelligence * Large language model
