Scaling External Knowledge Input Beyond Context Windows of LLMs via
  Multi-Agent Collaboration
Authors: name: Zijun Liu, name: Zhennan Wan, name: Peng Li, name: Ming Yan, name: Ji Zhang, name: Fei Huang, name: Yang Liu

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.