重新思考检索增强生成作为协作决策问题
Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem
- Jilin University（吉林大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出CoRAG框架，将检索器与生成器视为平等决策者，通过共同优化任务目标提升生成稳定性与泛化能力。
AI中文摘要：
检索增强生成（RAG）通过将语言生成与外部证据相结合，在知识密集型任务中展现了强大的有效性。尽管取得了成功，许多现有的RAG系统仍基于以排序为中心的、非对称依赖范式，其中生成器的生成质量高度依赖于排序器的重排序结果。为克服这一限制，我们提出了协作检索增强生成（CoRAG），一种将排序器和生成器视为平等决策者而非通过非对称依赖管道连接的框架。通过共同优化其行为以实现共享任务目标，排序器和生成器被鼓励合作，确保文档排序和生成工作协同以提高最终响应。实验结果表明，即使在仅训练约10K PopQA样本的情况下，CoRAG也表现出良好的泛化能力和改进的生成稳定性。
英文摘要：
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.
