RAC: Retrieval-Augmented Clarification for Faithful Conversational Search
This addresses the issue of corpus-faithful clarification for conversational search systems, which is an incremental improvement over prior work focused on fluency and intent alignment.
The paper tackled the problem of generating clarification questions in conversational search that are grounded in the underlying corpus to avoid unanswerable questions, and the result was that RAC significantly improved over baselines on four benchmarks, with novel metrics showing enhanced faithfulness.
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.