[2604.04593] Ruling Out to Rule In: Contrastive Hypothesis Retrieval for Medical Question Answering

Computer Science > Information Retrieval arXiv:2604.04593 (cs) [Submitted on 6 Apr 2026] Title:Ruling Out to Rule In: Contrastive Hypothesis Retrieval for Medical Question Answering Authors:Byeolhee Kim, Min-Kyung Kim, Young-Hak Kim, Tae-Joon Jeon View a PDF of the paper titled Ruling Out to Rule In: Contrastive Hypothesis Retrieval for Medical Question Answering, by Byeolhee Kim and 3 other authors View PDF HTML (experimental) Abstract:Retrieval-augmented generation (RAG) grounds large language models in external medical knowledge, yet standard retrievers frequently surface hard negatives that are semantically close to the query but describe clinically distinct conditions. While existing query-expansion methods improve query representation to mitigate ambiguity, they typically focus on enriching target-relevant semantics without an explicit mechanism to selectively suppress specific, clinically plausible hard negatives. This leaves the system prone to retrieving plausible mimics that overshadow the actual diagnosis, particularly when such mimics are dominant within the corpus. We propose Contrastive Hypothesis Retrieval (CHR), a framework inspired by the process of clinical differential diagnosis. CHR generates a target hypothesis $H^+$ for the likely correct answer and a mimic hypothesis $H^-$ for the most plausible incorrect alternative, then scores documents by promoting $H^+$-aligned evidence while penalizing $H^-$-aligned content. Across three medical QA benchmarks a...