用多源检索与引用生成提升大模型可信度，解决幻觉问题
MPR-CiteG: Enhancing RAG with Multi-Portfolio Retrieval and Citation-Grounded Generation
- 构建双模块系统：多组合检索+引用锚定生成
- 在ScienceON挑战中获第二名，有效减少生成错误
- 适合需要高可信度生成的科研与写作场景
本文提出MPR-CiteG框架，在ScienceON AI挑战中取得第二名，针对生成式AI中检索效率低和缺乏来源验证两大核心问题。该框架由双组件构成：多组合检索器（MPR）高效获取多样且相关的信息，引用锚定生成（CiteG）模块确保每个生成内容均事实一致并明确标注来源。实验验证了该方法在挑战数据集上的有效性与可靠性。代码已开源。
原文摘要 · Abstract (English)
This paper presents the MPR-CiteG framework, which achieved second place in the ScienceON AI Challenge by addressing two fundamental challenges in generative AI: inefficient retrieval and the absence of source verification. We propose a dual-component system, termed MPR-CiteG, in which the Multi-Portfolio Retriever (MPR) efficiently retrieves diverse and relevant information, while the Citation-Grounded Generation (CiteG) module ensures that every generated output remains factually consistent and explicitly attributed to its source. MPR-CiteG represents a significant step toward building more trustworthy and accurate LLMs that are not only capable of generating information but also of grounding their responses in reliable evidence, thereby mitigating common issues like model hallucination. Extensive experiments on the challenge dataset validate the effectiveness and reliability of our approach. Our code is available at https://github.com/2noweyh/MPR-citeG.
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