LongAgent: Scaling Language Models to 128k Context through Multi-Agent
  Collaboration
Authors: name: Jun Zhao, name: Can Zu, name: Hao Xu, name: Yi Lu, name: Wei He, name: Yiwen Ding, name: Tao Gui, name: Qi Zhang, name: Xuanjing Huang

Large language models (LLMs) have demonstrated impressive performance in
understanding language and executing complex reasoning tasks. However, LLMs
with long context windows have been notorious for their expensive training
costs and high inference latency. Even the most advanced models such as GPT-4
and Claude2 often make mistakes when processing inputs of over 100k tokens, a
phenomenon also known as lost in the middle. In this paper, we propose
LongAgent, a method based on multi-agent collaboration, which scales
LLMs (e.g., LLaMA) to a context of 128K and demonstrates potential superiority
in long-text processing compared to GPT-4. In LongAgent, a leader is
responsible for understanding user intent and directing team members to acquire
information from documents. Due to members' hallucinations, it is non-trivial
for a leader to obtain accurate information from the responses of dozens to
hundreds of members. To address this, we develop an inter-member
communication mechanism to resolve response conflicts caused by hallucinations
through information sharing. Our experimental results indicate that
LongAgent offers a promising alternative for long-text processing. The
agent team instantiated with LLaMA-7B achieves significant improvements in
tasks such as 128k-long text retrieval, multi-hop question answering, compared
to GPT-4.