Summary of Graphreader: Building Graph-based Agent to Enhance Long-context Abilities Of Large Language Models, by Shilong Li et al.
GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models
by Shilong Li, Yancheng He, Hangyu Guo, Xingyuan Bu, Ge Bai, Jie Liu, Jiaheng Liu, Xingwei Qu, Yangguang Li, Wanli Ouyang, Wenbo Su, Bo Zheng
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper introduces GraphReader, a graph-based agent system designed to tackle complex tasks that require processing long inputs. The system structures the input text into a graph and employs an agent to autonomously explore this graph, gathering information and optimizing its process until it can generate an answer. The approach outperforms GPT-4-128k on the LV-Eval dataset and four challenging benchmarks across context lengths from 16k to 256k. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GraphReader is a new way for computers to understand long texts by breaking them down into smaller pieces, like a map. This helps the computer find important information and answer questions better. In tests, GraphReader did much better than other systems at answering complex questions that required looking at lots of text. |
Keywords
* Artificial intelligence * Gpt
