Seventy3

Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。

今天的主题是：

Chain of Agents: Large Language Models Collaborating on Long-Context Tasks

Summary

Large language models (LLMs) struggle with long contexts due to limitations in processing extensive information. The "Chain-of-Agents" (CoA) framework addresses this by using multiple LLM agents that collaborate to process long documents. CoA divides the input into segments, assigns each segment to a worker agent, and then uses a manager agent to integrate the information and produce a final output. This method outperforms traditional approaches like Retrieval-Augmented Generation (RAG) and full-context LLMs, particularly in question answering, summarization, and code completion tasks. CoA also mitigates issues with focus within long contexts and is task-agnostic, training-free, and highly interpretable. Ultimately, the "Chain-of-Agents" framework facilitates improved processing and reasoning over long contexts, expanding the potential applications of LLMs in various domains.

大型语言模型（LLMs）在处理长上下文时面临困难，因为它们在处理大量信息时存在限制。为了应对这一挑战，"Chain-of-Agents"（CoA）框架通过使用多个LLM代理来协作处理长文档。CoA将输入划分为多个片段，将每个片段分配给一个工作代理，然后通过一个管理代理整合信息，最终生成输出。这种方法在问答、摘要和代码补全等任务中，特别是在处理长文档时，表现优于传统的检索增强生成（RAG）和全上下文LLM。CoA还解决了长上下文中的注意力问题，并且是任务无关的、无需训练的，并且具有高度的可解释性。最终，"Chain-of-Agents"框架通过提高长上下文的处理和推理能力，扩展了LLM在各个领域的潜在应用。

原文链接：https://arxiv.org/abs/2406.02818