Summary of Can Graph Learning Improve Planning in Llm-based Agents?, by Xixi Wu et al.
Can Graph Learning Improve Planning in LLM-based Agents?
by Xixi Wu, Yifei Shen, Caihua Shan, Kaitao Song, Siwei Wang, Bohang Zhang, Jiarui Feng, Hong Cheng, Wei Chen, Yun Xiong, Dongsheng Li
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 explores the emerging field of task planning in language agents, which aims to break down complex user requests into solvable sub-tasks. The researchers propose a novel approach using graph learning-based methods, particularly graph neural networks (GNNs), to enhance the performance of large language models (LLMs). They discover that the biases of attention and auto-regressive loss impede LLMs’ ability to effectively navigate decision-making on graphs, which is addressed by integrating GNNs with LLMs. The paper presents extensive experiments demonstrating that GNN-based methods surpass existing solutions even without training, with performance gains increasing with larger task graph sizes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how computers can help people make decisions. It’s like breaking down a big problem into smaller, easier ones to solve. They’re trying new ways to do this using special computer models called “graph neural networks”. These models are good at understanding relationships between things, which is helpful when making decisions. The researchers found that these models can actually help large language models (which understand and generate human-like text) make better decisions too! They tested their ideas and showed that it works really well. |
Keywords
» Artificial intelligence » Attention » Gnn
