RAC：基于检索的澄清以实现可靠的对话搜索
RAC: Retrieval-Augmented Clarification for Faithful Conversational Search
AI总结：
RAC通过检索增强的方法生成基于语料库的澄清问题，提高对话搜索的准确性与可靠性。
摘要：
澄清问题帮助对话搜索系统解决模糊或未明确规定的用户查询。尽管先前的工作重点在于流畅性和与用户意图的一致性，尤其是通过特征提取，但很少有关注将澄清建立在底层语料库上。没有这种基础，系统可能会提出无法从可用文档中回答的问题。我们引入RAC（检索增强澄清），一种生成基于语料库的澄清问题的框架。在比较几种检索策略后，我们微调大型语言模型以充分利用研究上下文，并鼓励生成有证据支持的问题。然后我们应用对比偏好优化，使支持检索段落的问题优于无根据的替代方案。在四个基准测试上评估，RAC在基线模型上显示出显著改进。除了LLM作为判断者评估外，我们引入了源自NLI和数据到文本的新型指标，以评估问题如何与上下文相关联，并证明我们的方法一致地提高了准确性。
英文摘要：
Clarification questions help conversational search systems resolve ambiguous or underspecified user queries. While prior work has focused on fluency and alignment with user intent, especially through facet extraction, much less attention has been paid to grounding clarifications in the underlying corpus. Without such grounding, systems risk asking questions that cannot be answered from the available documents. We introduce RAC (Retrieval-Augmented Clarification), a framework for generating corpus-faithful clarification questions. After comparing several indexing strategies for retrieval, we fine-tune a large language model to make optimal use of research context and to encourage the generation of evidence-based question. We then apply contrastive preference optimization to favor questions supported by retrieved passages over ungrounded alternatives. Evaluated on four benchmarks, RAC demonstrate significant improvements over baselines. In addition to LLM-as-Judge assessments, we introduce novel metrics derived from NLI and data-to-text to assess how well questions are anchored in the context, and we demonstrate that our approach consistently enhances faithfulness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
