GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models
Shilong Li, Yancheng He, Hangyu Guo, Xingyuan Bu, Ge Bai, Jie Liu, Jiaheng Liu, Xingwei Qu
Abstract
Long-context capabilities are essential for large language models (LLMs) to tackle complex and long-input tasks. Despite numerous efforts made to optimize LLMs for long contexts, challenges persist in robustly processing long inputs. In this paper, we introduce GraphReader, a graph-based agent system designed to handle long texts by structuring them into a graph and employing an agent to explore this graph autonomously. Upon receiving a question, the agent first undertakes a step-by-step analysis and devises a rational plan. It then invokes a set of predefined functions to read node content and neighbors, facilitating a coarse-to-fine exploration of the graph. Throughout the exploration, the agent continuously records new insights and reflects on current circumstances to optimize the process until it has gathered sufficient information to generate an answer. Experimental results on the LV-Eval dataset reveal that GraphReader using a 4k context window, consistently outperforms GPT-4-128k across context lengths from 16k to 256k by a large margin. Additionally, our approach demonstrates superior performance on four challenging single-hop and multi-hop benchmarks.
BibTeX
@inproceedings{li-etal-2024-graphreader,
title = "{G}raph{R}eader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models",
author = "Li, Shilong and
He, Yancheng and
Guo, Hangyu and
Bu, Xingyuan and
Bai, Ge and
Liu, Jie and
Liu, Jiaheng and
Qu, Xingwei and
Li, Yangguang and
Ouyang, Wanli and
Su, Wenbo and
Zheng, Bo",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.746/",
doi = "10.18653/v1/2024.findings-emnlp.746",
pages = "12758--12786"
}