让检索和生成协同决策，提升问答系统稳定性
Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem
- 将检索器与生成器视为平级决策者，共同优化目标
- 仅用1万条数据训练仍保持良好泛化能力
- 适合需要稳定输出的对话与知识密集型任务
检索增强生成（RAG）在知识密集型任务中表现出色，通过外部证据来约束语言生成。然而，现有RAG系统多采用以排序为中心的非对称依赖范式，生成质量高度依赖重排结果。为克服此局限，我们提出协作式检索增强生成（CoRAG），将重排器与生成器视为平等决策者，而非通过单向依赖链连接。通过联合优化二者行为以达成共享任务目标，促使文档重排与生成协同进行，从而提升最终响应质量。实验表明，即使仅在约1万条PopQA样本上训练，CoRAG也展现出良好的泛化能力和生成稳定性。模型代码已开源：https://github.com/CoderrrSong/CoRAG。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) has demonstrated strong effectiveness in knowledge-intensive tasks by grounding language generation in external evidence. Despite its success, many existing RAG systems are built based on a ranking-centric, asymmetric dependency paradigm, where the generation quality of the generator is highly dependent on reranking results of the reranker. To overcome this limitation, we propose Cooperative Retrieval-Augmented Generation (CoRAG), a framework that treats the reranker and the generator as peer decision-makers rather than being connected through an asymmetric dependency pipeline. By jointly optimizing their behaviors toward a shared task objective, the reranker and generator are encouraged to cooperate, ensuring that document reranking and generation work in concert to improve the final response. Experimental results demonstrate good generalization and improved generation stability of CoRAG, even when the model is trained on only around 10K PopQA samples. Our model released in https://github.com/CoderrrSong/CoRAG.
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