让大模型同时用好内部知识和外部检索，提升问答准确率
CoCoA: Collaborative Chain-of-Agents for Parametric-Retrieved Knowledge Synergy
- 设计多智能体协作链，分步完成知识推理与答案生成
- 在开放域和多跳问答任务中，准确率显著优于现有方法
- 适合需要融合多种知识源的复杂问答场景
检索增强生成（RAG）能提升大语言模型在知识密集型任务中的表现，但当前方法难以充分协同利用模型内部参数化知识与外部检索知识。检索内容可能误导生成，而生成过程也可引导模型输出更准确结果。为此，我们提出协同链式智能体框架CoCoA，先通过CoCoA-zero进行条件知识归纳与推理，再基于此构建长链训练策略，从CoCoA-zero中合成多智能体推理轨迹以微调大模型。该策略增强了模型显式整合并联合使用参数化与检索知识的能力。实验表明，CoCoA在开放域问答和多跳问答任务中均表现优异。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs), especially for knowledge-intensive tasks. Despite its advantages, current RAG methods often struggle to fully exploit knowledge during generation. In particular, the synergy between the model's internal parametric knowledge and external retrieved knowledge remains limited. Retrieved contents may sometimes mislead generation, while certain generated content can guide the model toward more accurate outputs. In this work, we propose Collaborative Chain-of-Agents, a framework designed to enhance explicitly synergy over both parametric and retrieved knowledge. Specifically, we first introduce CoCoA-zero, a multi-agent RAG framework that first performs conditional knowledge induction and then reasons answers. Building on this, we develop CoCoA, a long-chain training strategy that synthesizes extended multi-agent reasoning trajectories from CoCoA-zero to fine-tune the LLM. This strategy enhances the model's capability to explicitly integrate and jointly leverage parametric and retrieved knowledge. Experimental results demonstrate the superiority of CoCoA in open-domain QA and multi-hop QA.
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