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

Byeolhee Kim; Min-Kyung Kim; Young-Hak Kim; Tae-Joon Jeon

cs.IRApr 6, 2026

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

Authors: Byeolhee Kim, Min-Kyung Kim, Young-Hak Kim, Tae-Joon Jeon

Organizations: Department of Information Medicine, Asan Medical Center, Seoul, Republic of Korea · Department of Computer Science, Yonsei University, Seoul, Republic of Korea · INMED DATA, Seoul, Republic of Korea · Division of Cardiology, Department of Information Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

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 and three answer generators, CHR outperforms all five baselines in every configuration, with improvements of up to 10.4 percentage points over the next-best method. On the $n=587$ pooled cases where CHR answers correctly while embedded hypothetical-document query expansion does not, 85.2% have no shared documents between the top-5 retrieval lists of CHR and of that baseline, consistent with substantive retrieval redirection rather than light re-ranking of the same candidates. By explicitly modeling what to avoid alongside what to find, CHR bridges clinical reasoning with retrieval mechanism design and offers a practical path to reducing hard-negative contamination in medical RAG systems.

Figures & tables

Figure 1. Overview of Contrastive Hypothesis Retrieval (CHR). Given a clinical question, CHR generates a contrastive hypothesis pair consisting of a target hypothesis ( $H^{+}$ ) and a mimic hypothesis ( $H^{-}$ ), retrieves documents using contrastive scoring that promotes target-aligned content while penalizing mimic-aligned content, and generates the final answer from the retrieved evidence.

Panel of Figure 2 — Figure 2. Prompt template for contrastive hypothesis generation. The system prompt establishes the role of a medical specialist, and the user prompt instructs the model to generate a target hypothesis ( $H^{+}$ ) for the likely correct diagnosis and a mimic hypothesis ( $H^{-}$ ) for the most plausible incorrect alternative.

Generator	Method	MMLU-Med	MedQA-US	BioASQ-Y/N	Avg.
Llama-3-8B-Instruct	Standard RAG	36.8	36.1	41.8	38.2
	HyDE ( $H^{+}$ only)	47.2	44.6	51.3	47.7
	Query2doc	49.1	46.2	46.9	47.4
	CSQE	49.7	46.5	56.5	50.9
	ThinkQE	41.3	45.6	52.1	46.3
	CHR (Ours)	50.4	51.5	63.1	55.0

Table 1. Accuracy (%) across three medical QA benchmarks. All query-expansion baselines use the same hypothesis generator (Qwen2.5-72B-Instruct), retriever (MedCPT), and corpus (MedCorp). CHR consistently achieves the highest accuracy across all settings.

Retriever	Std RAG	HyDE	CHR	Std RAG	HyDE	CHR	Std RAG	HyDE	CHR	Avg.
	MedQA			BioASQ			MMLU-Med
MedCPT	36.1	44.6	51.5	41.8	51.3	63.1	36.8	47.2	50.4	47.0
BGE-large-en-v1.5	41.8	52.4	52.8	41.8	53.1	60.2	52.3	54.5	56.6	51.7
E5-large-v2	40.2	50.6	52.3	45.5	40.5	62.6	51.7	55.2	57.7	50.7
BM25	40.1	51.5	53.3	46.8	65.2	63.4	57.9	59.3	59.9	55.3

Table 2. Retriever-agnosticism. Accuracy (%) on three medical QA benchmarks with Llama-3-8B-Instruct as the answer generator and Qwen2.5-72B-Instruct as the hypothesis generator, sweeping four retrievers spanning dense (MedCPT, BGE, E5) and sparse (BM25) families. Bold marks the best method within each (retriever, dataset) triple. The Avg. column averages all nine method–benchmark scores within each retriever row. CHR wins on MedQA and MMLU-Med across all four retrievers and on BioASQ for three of four retrievers; the only exception is BM25, where HyDE is slightly higher.

Retriever	$\lambda$ schedule	MedQA	BioASQ	MMLU	Avg.
MedCPT	Fixed ( $\lambda{=}1.0$ )	51.5	63.1	50.4	55.0
	Adaptive (linear)	51.7	69.4	56.1	59.1
	Adaptive (threshold)	50.3	70.4	57.2	59.3
	Adaptive (sigmoid)	53.3	68.8	57.2	59.8
BGE	Fixed ( $\lambda{=}1.0$ )	52.8	60.2	56.6	56.5
	Adaptive (linear)	55.5	65.7	58.4	59.9

Table 3. Adaptive contrastive weighting versus fixed

\lambda

. Accuracy (%) with Llama-3-8B-Instruct as the answer generator and Qwen2.5-72B-Instruct as the hypothesis generator. All three adaptive schedules outperform the fixed weight on average for both retrievers, with a best-schedule improvement of

+4.8

pp on MedCPT and

+3.4

pp on BGE.

Dataset	n	Zero Overlap	Mean Overlap
MMLU-Med	172	78.5%	0.25
MedQA	249	80.3%	0.25
BioASQ	166	99.4%	0.01
Combined	587	85.2%	0.18

Table 4. Retrieval divergence analysis for cases where CHR succeeds but HyDE fails (

n=587

). Zero Overlap indicates the percentage of cases with no shared documents in top-5 results. Mean Overlap is the average overlap ratio of top-5 results between CHR and HyDE.

Method	LLM Calls	Output Tokens	Token Reduction
Standard RAG	0	0	N/A
Query2Doc	1	$\sim$ 512	5.7 $\times$
CSQE	1 + retrieval	$\sim$ 480	6.1 $\times$
ThinkQE	$\geq$ 3	$\sim$ 1,500	2.0 $\times$
HyDE	$\sim$ 8	$\sim$ 2,926	1.0 $\times$
CHR (Ours)	1	$\sim$ 303	9.7 $\times$

Table 5. Query-expansion cost per sample, averaged across MedQA and MMLU-Med. Output token counts are estimated from generated text length. Token Reduction is relative to HyDE (most expensive). CHR achieves the lowest cost while consistently achieving the highest accuracy.

$H^{-}$ Quality	$n$	Correct	Accuracy
Excellent	9	6	66.7%
Good	30	12	40.0%
Poor	8	3	37.5%

Table 6. CHR accuracy stratified by physician-rated mimic hypothesis quality. Samples where

H^{+}

was factually incorrect are excluded (

n{=}47

Panel of Figure 3 — Figure 3. Case study showing why the negative (mimic) hypothesis $H^{-}$ is essential for discriminative retrieval. In the left box, bolded text highlights misleading co-occurrences of antivirals with parkinsonian symptoms (as side effects, not treatments), which led to the incorrect answer. In the right box, bolded text highlights the key sentences identifying amantadine as an antiviral repurposed for Parkinson’s disease, directly leading to the correct answer.

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Panel of Figure 4 — Figure 4. Additional case study from MedQA (oncology domain). In the left box, bolded text highlights how retrieved documents focus on tamoxifen-related gynecologic bleeding, the dominant complication in the tamoxifen safety literature, which led to the incorrect answer. In the right box, bolded text highlights evidence describing tamoxifen’s pro-coagulant effects via hepatic estrogen agonism, directly supporting deep venous thrombosis as the correct answer.

Panel of Figure 5 — Figure 5. Sensitivity of CHR accuracy to the contrastive weight $\lambda$ on MedQA (Gemma-2-9B-It). The dashed red lines indicate the robust plateau region $\lambda\in[0.6,1.2]$ where CHR consistently outperforms both baselines. Performance peaks at $\lambda=1.0$ .

Question (MedQA #16): A 7-year-old boy with seasonal allergic asthma is being considered for an experimental therapy targeting a mediator of antibody class switching. Which mediator is described?

Options: (A) IL-2 (B) IL-10 (C) IL-13 (D) IL-4

\checkmark

H^{+}

: IL-4 is the key cytokine driving Th2 differentiation and B-cell class switching to IgE .

H^{-}

: IL-13 overlaps functionally with IL-4 but does not directly drive B-cell class switching to IgE .

Prediction: CHR

\rightarrow

\rightarrow

(D) IL-4 (Correct)

CHR top evidence: (1) Co-occurring IL-4 and IL-13 with IgE switching. (2) “ IL-13, another switch factor for IgE …” (3) “Both IL13 and IL4 are capable of inducing class switching…” HyDE top evidence: (1) “B cells are switched to IgE by IL-4 …” (2) Th2-focused evidence emphasizing IL-4 -targeted interventions. (3) Receptor-level discussion (IL-4/IL-13 axis) with clearer IL-4 role.

Analysis: Strong IL-4/IL-13 co-occurrence causes partial overlap between target and mimic directions; subtracting

\lambda\mathbf{H}^{-}

can therefore suppress IL-4-relevant evidence and make CHR’s context more ambiguous than HyDE’s.

Appendix

Table 7. Failure case of semantic co-occurrence collapse. CHR correctly identifies IL-13 as the mimic for IL-4, but heavy corpus co-occurrence of these cytokines in the same Th2 pathway causes the contrastive subtraction to suppress some target-relevant evidence.

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Retrieval-Augmented Generation (RAG) improves Large Language Models (LLMs) by grounding generation in external, non-parametric knowledge. However, when a task requires choosing among competing options, simply grounding generation in broadly relevant context is often insufficient to drive the final decision. Existing RAG methods typically rely on a single initial query, which often favors topical relevance over decision-relevant evidence, and therefore retrieves background information that can fail to discriminate among answer options. To address this issue, here we propose Hypothesis-Conditioned Query Rewriting (HCQR), a training-free pre-retrieval framework that reorients RAG from topic-oriented retrieval to evidence-oriented retrieval. HCQR first derives a lightweight working hypothesis from the input question and candidate options, and then rewrites retrieval into three targeted queries that seek evidence to: (1) support the hypothesis, (2) distinguish it from competing alternatives, and (3) verify salient clues in the question. This approach enables context retrieval that is more directly aligned with answer selection, allowing the generator to confirm or overturn the initial hypothesis based on the retrieved evidence. Experiments on MedQA and MMLU-Med show that HCQR consistently outperforms single-query RAG and re-rank/filter baselines, improving average accuracy over Simple RAG by 5.9 and 3.6 points, respectively. Code is available at https://anonymous.4open.science/r/HCQR-1C2E.

Abstract

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