From Topical Relevance to Answerability: Entailment Distillation for Conversational Retrieval

• URL: http://arxiv.org/abs/2609.03482v1
• Date: Thu, 03 Sep 2026 07:38:36 GMT
• Title: From Topical Relevance to Answerability: Entailment Distillation for Conversational Retrieval
• Abstract summary: We propose CLEAR, a framework that shifts conversational retrieval from topical relevance to answerability.<n>The core of CLEAR is entailment distillation, which transfers answer-passage entailment supervision into a cross-encoder reranker.<n> CLEAR consistently improves top-ranked precision over strong query-rewriting and dense-retrieval baselines.
• Score: 24.372303326753535
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing conversational retrievers commonly treat topical relevance as a proxy for answerability. However, a passage that closely matches the dialogue context is not necessarily the one that supports the correct answer. We identify this mismatch as a systematic answerability gap. To address this issue, we propose CLEAR, a framework that shifts conversational retrieval from topical relevance to answerability. The core of CLEAR is entailment distillation, which transfers answer-passage entailment supervision into a cross-encoder reranker so that the reranker discriminates answer-supporting passages from topical distractors at inference time, without requiring answers. CLEAR is complemented by a passage-centric abductive recall module that brings low-similarity yet answerable passages into the candidate pool by inferring answerable queries from passages with an LLM. Across TopiOCQA, QReCC, and out-of-domain TREC CAsT datasets, CLEAR consistently improves top-ranked precision over strong query-rewriting and dense-retrieval baselines, with the largest gains observed in conversations involving heavier topical noise. Moreover, applying our reranker on top of an LLM-driven query rewriter yields further gains.

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