用信息片段提升问答准确性和完整性，适合追求事实正确的场景。
UiS-IAI@LiveRAG: Retrieval-Augmented Information Nugget-Based Generation of Responses
- 将文档拆成最小信息单元，分步处理以确保每句有据可依。
- 多轮查询重写提升召回率，但过度使用文档会降低效果。
- 适合需要精准溯源与全面覆盖的生成任务，尤其关注长文本质量。
检索增强生成（RAG）在事实准确性、来源归属和响应完整性方面面临挑战。2025年SIGIR举办的LiveRAG挑战赛采用固定语料库和共享开源大模型推进研究。我们提出一种模块化流水线，基于从检索文档中提取的信息片段（information nuggets）进行处理。该多阶段流程包括查询重写、段落检索与重排序、片段检测与聚类、聚类排序与摘要、以及响应流畅性优化。此设计天然支持事实锚定，便于来源标注，并在长度限制下实现最大信息覆盖。本工作扩展了此前的多维度查询重写成果，聚焦于检索组件改进；在生成环节则强化上下文筛选能力，力求在保证效率的前提下扩大信息覆盖面。实验表明，结合原始查询与少量子查询重写可提升召回率，但超过一定阈值增加用于重排序和生成的文档数量反而降低效果，未带来响应质量提升。
原文摘要 · Abstract (English)
Retrieval-augmented generation (RAG) faces challenges related to factual correctness, source attribution, and response completeness. The LiveRAG Challenge hosted at SIGIR'25 aims to advance RAG research using a fixed corpus and a shared, open-source LLM. We propose a modular pipeline that operates on information nuggets-minimal, atomic units of relevant information extracted from retrieved documents. This multistage pipeline encompasses query rewriting, passage retrieval and reranking, nugget detection and clustering, cluster ranking and summarization, and response fluency enhancement. This design inherently promotes grounding in specific facts, facilitates source attribution, and ensures maximum information inclusion within length constraints. In this challenge, we extend our focus to also address the retrieval component of RAG, building upon our prior work on multi-faceted query rewriting. Furthermore, for augmented generation, we concentrate on improving context curation capabilities, maximizing the breadth of information covered in the response while ensuring pipeline efficiency. Our results show that combining original queries with a few sub-query rewrites boosts recall, while increasing the number of documents used for reranking and generation beyond a certain point reduces effectiveness, without improving response quality.
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