Summary of On the Roles Of Llms in Planning: Embedding Llms Into Planning Graphs, by Hankz Hankui Zhuo and Xin Chen and Rong Pan
On the Roles of LLMs in Planning: Embedding LLMs into Planning Graphs
by Hankz Hankui Zhuo, Xin Chen, Rong Pan
First submitted to arxiv on: 18 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper investigates the role of large language models (LLMs) in off-the-shelf planning frameworks, aiming to leverage their emergent planning capabilities. By embedding LLMs into a graph-based planning framework, the authors propose a novel LLMs-based planning approach that utilizes LLMs at two levels: mutual constraints generation and constraints solving. The effectiveness of this framework is empirically demonstrated across various planning domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to use large language models (LLMs) for planning. It looks at putting LLMs into a type of planning called graph-based planning. This helps LLMs make better plans by generating and solving constraints. The authors show that this works well in different planning situations. |
Keywords
» Artificial intelligence » Embedding
