通过多智能体协作扩展大语言模型上下文窗口之外的外部知识输入

Scaling External Knowledge Input Beyond Context Windows of LLMs via Multi-Agent Collaboration

May 27, 2025

作者: Zijun Liu, Zhennan Wan, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu

cs.AI

摘要

随着推理与信息检索后处理技术的飞速发展，大型语言模型（LLMs）能够整合大量检索到的知识来解决复杂任务。然而，LLMs有限的上下文窗口阻碍了外部知识输入的规模扩展，特别是在需要大量外部知识的任务中，这一限制尤为明显，阻碍了性能的进一步提升。现有的上下文窗口扩展方法不可避免地会导致信息丢失。基于LLM的多智能体方法作为一种新范式应运而生，它以分布式方式处理海量输入，在此过程中，我们识别出现有知识同步与推理流程中的两大核心瓶颈。本研究中，我们开发了一个多智能体框架——ExtAgents，旨在突破这些瓶颈，在不依赖长上下文训练的情况下，实现推理时知识整合的更好扩展性。通过我们增强的多跳问答测试集$boldsymbol{inftyBench+}$以及其他包括长篇调查生成在内的公开测试集的评估，ExtAgents在同等外部知识输入量下，无论是否超出上下文窗口，均显著超越了现有非训练方法的性能表现。此外，得益于高度并行化，该方法保持了高效性。未来在增加外部知识输入时对LLM智能体协调机制的深入研究，有望为实际应用带来更大益处。

English

With the rapid advancement of post-training techniques for reasoning and information seeking, large language models (LLMs) can incorporate a large quantity of retrieved knowledge to solve complex tasks. However, the limited context window of LLMs obstructs scaling the amount of external knowledge input, prohibiting further improvement, especially for tasks requiring significant amount of external knowledge. Existing context window extension methods inevitably cause information loss. LLM-based multi-agent methods emerge as a new paradigm to handle massive input in a distributional manner, where we identify two core bottlenecks in existing knowledge synchronization and reasoning processes. In this work, we develop a multi-agent framework, ExtAgents, to overcome the bottlenecks and enable better scalability in inference-time knowledge integration without longer-context training. Benchmarked with our enhanced multi-hop question answering test, $boldsymbol{inftyBench+}, and other public test sets including long survey generation, ExtAgents significantly enhances the performance over existing non-training methods with the same amount of external knowledge input, regardless of whether it falls within or exceeds the context window$. Moreover, the method maintains high efficiency due to high parallelism. Further study in the coordination of LLM agents on increasing external knowledge input could benefit real-world applications.