Report
GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models
| العنوان: | GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models |
|---|---|
| المؤلفون: | Li, Shilong, He, Yancheng, Guo, Hangyu, Bu, Xingyuan, Bai, Ge, Liu, Jie, Liu, Jiaheng, Qu, Xingwei, Li, Yangguang, Ouyang, Wanli, Su, Wenbo, Zheng, Bo |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence |
| الوصف: | 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. Comment: [EMNLP 2024] The first four authors contributed equally, 29 pages |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.14550 |
| رقم الانضمام: | edsarx.2406.14550 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |