Summary of Graphagent: Agentic Graph Language Assistant, by Yuhao Yang et al.
GraphAgent: Agentic Graph Language Assistant
by Yuhao Yang, Jiabin Tang, Lianghao Xia, Xingchen Zou, Yuxuan Liang, Chao Huang
First submitted to arxiv on: 22 Dec 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 A new automated agent pipeline, GraphAgent, is introduced to tackle complex data relationships in both structured and unstructured formats. The pipeline addresses explicit graph dependencies and implicit interdependencies among semantic entities, suitable for predictive tasks like node classification and generative tasks such as text generation. GraphAgent consists of three components: a Graph Generator Agent that builds knowledge graphs, a Task Planning Agent that interprets user queries and formulates corresponding tasks, and a Task Execution Agent that executes planned tasks while automating tool matching and invocation. By integrating language models with graph language models, GraphAgent uncovers intricate relational information and data semantic dependencies. Experimental results on diverse datasets demonstrate the effectiveness of GraphAgent across various settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GraphAgent is an automated agent pipeline that helps computers understand complex relationships in both structured and unstructured data formats. This is important because real-world data often has many connections between different pieces of information, which can be hard for computers to understand. GraphAgent has three parts: one that builds a knowledge graph to show these connections, another that plans what tasks to do based on user requests, and a third that carries out those tasks while automating tool use. By combining language models with graph models, GraphAgent can uncover hidden patterns in data. The authors tested GraphAgent on many different datasets and showed it works well. |
Keywords
» Artificial intelligence » Classification » Knowledge graph » Text generation
