多轮问答中用稀疏检索与大模型重排提升跨领域鲁棒性。
uva-irlab-conv at SemEval-2026 Task 8: Multi-Turn RAG with Learned Sparse Retrieval and Listwise Reranking
- 用学习的稀疏检索结合大模型重排，融合对话历史增强检索。
- 在金融、政府等4个领域表现稳定，对无答案查询有识别能力。
- 适合需要多轮交互和跨域泛化的智能客服场景。
本报告介绍我们在 SemEval-2026 任务8（多轮检索与问答）中的参与情况。该任务评估对话系统在金融、云文档、政府、维基百科四个领域的表现，并包含无法回答的查询（即语料库中缺乏足够证据）。我们提出一种多轮检索增强生成流程，结合学习的稀疏检索与基于大语言模型的重排与生成。以稀疏检索作为主检索方式，利用其在各领域间良好的泛化能力；同时利用大模型的长上下文能力，实现基于完整对话历史的查询重写、点对点与列表式重排以及最终响应生成。这种多步设计使对话上下文在检索与生成阶段有效融合，提升了跨领域的鲁棒性。
原文摘要 · Abstract (English)
This report describes our participation in SemEval-2026 Task 8 on multi-turn retrieval and question answering. The task evaluates conversational systems across four domains (finance, cloud documentation, government, Wikipedia), and includes unanswerable queries where the available collection does not contain sufficient evidence to produce a complete response. We propose a multi-turn retrieval-augmented generation pipeline that combines learned sparse retrieval with LLM-based reranking and generation. Using sparse retrieval as the primary retrieval method, we leverage its strong generalization across domains. In addition, we make use of the long-context capabilities of LLMs for conversational query rewriting, pointwise and listwise reranking, and generating the final response, each conditioned on the full conversational history. This multi-step design enables effective integration of conversational context throughout retrieval and generation, improving robustness across domains.
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