让AI同时回答科学问题中的多个意图，提升证据覆盖与准确性。
MuISQA: Multi-Intent Retrieval-Augmented Generation for Scientific Question Answering
- 用大模型拆解问题为多意图查询，针对性检索
- 通过RRF融合结果，提升跨意图证据覆盖率
- 在复杂科学问答中显著优于传统方法
复杂科学问题常包含多个意图，如识别基因突变并关联疾病。这类任务需来自不同来源的证据和多跳推理，而传统检索增强生成（RAG）系统多为单意图设计，导致证据覆盖不全。为此，我们提出多意图科学问答（MuISQA）基准，用于评估RAG系统在子问题间异构证据覆盖的表现。同时，我们设计了一种意图感知的检索框架：利用大语言模型（LLMs）推测潜在答案，分解为特定意图的查询，并检索对应支持段落。检索片段经互斥排名融合（RRF）聚合与重排序，以平衡多意图覆盖并减少冗余。在MuISQA基准及多个通用RAG数据集上的实验表明，该方法在检索准确率和证据覆盖方面持续优于传统方法。
原文摘要 · Abstract (English)
Complex scientific questions often entail multiple intents, such as identifying gene mutations and linking them to related diseases. These tasks require evidence from diverse sources and multi-hop reasoning, while conventional retrieval-augmented generation (RAG) systems are usually single-intent oriented, leading to incomplete evidence coverage. To assess this limitation, we introduce the Multi-Intent Scientific Question Answering (MuISQA) benchmark, which is designed to evaluate RAG systems on heterogeneous evidence coverage across sub-questions. In addition, we propose an intent-aware retrieval framework that leverages large language models (LLMs) to hypothesize potential answers, decompose them into intent-specific queries, and retrieve supporting passages for each underlying intent. The retrieved fragments are then aggregated and re-ranked via Reciprocal Rank Fusion (RRF) to balance coverage across diverse intents while reducing redundancy. Experiments on both MuISQA benchmark and other general RAG datasets demonstrate that our method consistently outperforms conventional approaches, particularly in retrieval accuracy and evidence coverage.
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