混合检索与查询重写，提升多轮RAG的准确率
Sifei at SemEval-2026 Task 8: Hybrid Retrieval and Query Rewriting for Multi-Turn RAG
- 结合稠密与稀疏检索，通过可控查询重写增强召回
- 任务A得0.5453 nDCG@5，优于最强基线0.4795
- 无需训练，组件开源，适合快速部署多轮问答系统
多轮检索增强生成（RAG）因用户意图演变、对话噪声和严格上下文限制而具有挑战性。我们为SemEval-2026任务8提出一种无需训练的混合检索流程，结合稠密与稀疏检索，辅以受控查询重写和交叉编码器重排序。在任务A的官方测试集上，系统取得0.5453 nDCG@5，排名38支队伍中的第3，超过最强基线0.4795。对于任务C，复用任务A的文档检索结果，采用轻量级生成流程并遵循官方提示，获得相关性与忠实度的调和平均值0.5312，在29支队伍中排名第15。所有检索组件开源，查询重写与答案生成依赖LLM API。
原文摘要 · Abstract (English)
Multi-turn retrieval-augmented generation (RAG) is challenging due to evolving user intent, conversational noise, and strict context limits. We propose a training-free hybrid retrieval pipeline for SemEval-2026 Task 8 that combines dense and sparse retrieval with controlled query rewriting and cross-encoder reranking. On the official test set of Task A, our system achieves 0.5453 nDCG@5, ranking third among 38 teams and outperforming the strongest baseline score of 0.4795. For Task C, we reuse the documents retrieved for Task A and apply a lightweight generation pipeline guided by the official prompt, achieving 0.5312 as the harmonic mean of relevance and faithfulness and ranking 15th among 29 teams. All retrieval components are open-source, while query rewriting and answer generation rely on LLM APIs.
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