arxiv: 2604.10937 · v2 · submitted 2026-04-13 · 💻 cs.IR

Recognition: unknown

Benchmarking and Enabling Efficient Chinese Medical Retrieval via Asymmetric Encoders

Angqing Jiang , Jianlyu Chen , Zhe Fang , Yongcan Wang , Xinpeng Li , Keyu Ding , Defu Lian

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:35 UTC · model grok-4.3

classification 💻 cs.IR

keywords Chinese medical retrievalasymmetric encoderstext embedding benchmarkretrieval latencytwo-stage trainingLLM embeddingsCMedTEB

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The pith

An asymmetric encoder pair outperforms symmetric models for Chinese medical retrieval without raising latency.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces CMedTEB, a benchmark for Chinese medical embedding tasks covering retrieval, reranking, and semantic similarity, built with multi-LLM voting and clinical expert validation to reduce label noise. It then presents CARE, an asymmetric retriever that uses a lightweight BERT-style encoder for real-time queries and a stronger LLM encoder for offline documents. A two-stage training process progressively aligns the representations from these structurally different encoders. This combination delivers better retrieval results than existing symmetric models while preserving the low latency required for practical deployment.

Core claim

The Chinese Medical Text Embedding Benchmark (CMedTEB) supplies high-fidelity labels across retrieval, reranking, and STS tasks through a multi-LLM voting pipeline validated by clinical experts. CARE, the proposed asymmetric architecture, pairs a lightweight BERT-style encoder for online query encoding with a powerful LLM-based encoder for offline document encoding and applies a novel two-stage training strategy to bridge representation gaps, achieving superior retrieval performance over state-of-the-art symmetric models on CMedTEB without increasing inference latency.

What carries the argument

The CARE asymmetric architecture, which assigns a fast lightweight encoder to queries and a capable LLM encoder to documents, bridged by two-stage training that progressively aligns their representations.

If this is right

CARE achieves higher retrieval accuracy than symmetric models on CMedTEB while keeping inference latency unchanged.
The two-stage training strategy enables effective use of structurally dissimilar encoders in one system.
CMedTEB provides a standardized, low-noise testbed for advancing Chinese medical text embedding work across retrieval, reranking, and STS.
Real-time medical search applications can adopt stronger document encoders without paying a speed penalty.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same asymmetric pattern could be tested in other languages or medical subdomains where query speed matters.
Different lightweight-LLM encoder combinations could be swapped in to measure further gains on the same benchmark.
The benchmark construction method might generalize to create high-quality test sets for non-medical specialized retrieval.

Load-bearing premise

The multi-LLM voting pipeline with clinical expert validation produces labels accurate enough to serve as gold standard, and the two-stage training fully compensates for the structural differences between the two encoders.

What would settle it

A direct comparison on CMedTEB showing CARE's retrieval metrics falling below those of the strongest symmetric baseline or its query encoding latency exceeding the baseline.

Figures

Figures reproduced from arXiv: 2604.10937 by Angqing Jiang, Defu Lian, Jianlyu Chen, Keyu Ding, Xinpeng Li, Yongcan Wang, Zhe Fang.

**Figure 1.** Figure 1: Efficiency-performance trade-off on CMedTEB Retrieval. The x-axis shows queries per second (QPS) on a single A100 80GB GPU, while the y-axis reports nDCG@10. Notably, CARE breaks the conventional trade-off: it matches the high retrieval quality of heavy LLM-based models while sustaining the high throughput of lightweight BERT-style models. which leverage external knowledge to enhance large language models … view at source ↗

**Figure 2.** Figure 2: Workflow for constructing the CMedTEB benchmark. The Figure shows the distinct curation strategies for discriminative tasks (Retrieval/Rerank) and semantic similarity tasks (STS). Task Test Train Main Metric New tasks Retrieval 734 20,000 nDCG@10 Rerank 1,128 MAP@10 Synonym STS 5,000 10,000 Pearson Public datasets CMedQA-v1-rk. 1,000 50,000 MAP@10 CMedQA-v2-rk. 1,000 MAP@10 [PITH_FULL_IMAGE:figures/full_f… view at source ↗

**Figure 3.** Figure 3: Inference and Training pipeline for asymmetric embedding model. Stage I: Query encoder is trained to align with the frozen document encoder using Asym-InfoNCE and MSE losses. Stage II: Both encoders are jointly fine-tuned with Asym-InfoNCE loss on retrieval data. 4.2.2 Asymmetric Stage II: Joint Fine-tuning After alignment, we unfreeze both encoders and perform end-to-end joint fine-tuning. The goal of thi… view at source ↗

**Figure 4.** Figure 4: visualizes the performance trade-off between symmetric (Med-Emb) and asymmetric (CARE) architectures. While symmetric models (Med-Emb-4B/8B) set a high performance ceiling, they incur prohibitive computational costs, indicated by the sharp spike in the blue line. In contrast, CARE strikes a balance: CARE-0.3B-8B achieves an average score of 65.21, trailing the fully symmetric 8B giant (65.63) by only 0.… view at source ↗

read the original abstract

Effective medical text retrieval requires both high accuracy and low latency. While LLM-based embedding models possess powerful retrieval capabilities, their prohibitive latency and high computational cost limit their application in real-time scenarios. Furthermore, the lack of comprehensive and high-fidelity benchmarks hinders progress in Chinese medical text retrieval. In this work, we introduce the Chinese Medical Text Embedding Benchmark (CMedTEB), a benchmark spanning three kinds of practical embedding tasks: retrieval, reranking, and semantic textual similarity (STS). Distinct from purely automated datasets, CMedTEB is curated via a rigorous multi-LLM voting pipeline validated by clinical experts, ensuring gold-standard label quality while effectively mitigating annotation noise. On this foundation, we propose the Chinese Medical Asymmetric REtriever (CARE), an asymmetric architecture that pairs a lightweight BERT-style encoder for online query encoding with a powerful LLM-based encoder for offline document encoding. However, optimizing such an asymmetric retriever with two structurally different encoders presents distinctive challenges. To address this, we introduce a novel two-stage training strategy that progressively bridges the query and document representations. Extensive experiments demonstrate that CARE surpasses state-of-the-art symmetric models on CMedTEB, achieving superior retrieval performance without increasing inference latency.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

CMedTEB and CARE add a domain benchmark and asymmetric retriever for Chinese medical text, but missing label agreement numbers weaken the performance claims.

read the letter

The paper's main contributions are the CMedTEB benchmark for Chinese medical retrieval, reranking, and STS, plus the CARE asymmetric model that pairs a lightweight query encoder with a heavier document encoder trained in two stages to close the representation gap. This targets a real need for accurate yet fast medical search in Chinese, where latency constraints matter in practice. The curation via multi-LLM voting plus expert validation is a step up from purely synthetic data, and the two-stage training is a straightforward way to make the asymmetric setup work without obvious collapse. The work is honest about the efficiency goal and shows the architecture can match or beat symmetric baselines on their new test set without added inference cost. The soft spot is the label quality. The abstract describes the voting pipeline and expert check but supplies no agreement rates, threshold details, or disagreement resolution steps. That leaves open the chance that residual noise favors the stronger document encoder or the particular training schedule, which directly affects how much credit the gains deserve. Experimental tables are also light on baseline lists, splits, and significance tests in the summary. This paper is for people working on domain-specific embedding models or medical IR in non-English settings. A reader building retrieval systems for specialized corpora can use the benchmark and the asymmetric idea as a starting point. It has enough new artifacts and a clear engineering focus to deserve peer review, though reviewers will need to press for the missing annotation stats and ablations before the results can be taken as solid.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces the Chinese Medical Text Embedding Benchmark (CMedTEB), spanning retrieval, reranking, and STS tasks and constructed via a multi-LLM voting pipeline validated by clinical experts to ensure high-fidelity labels. It proposes CARE, an asymmetric architecture using a lightweight BERT-style encoder for queries and a powerful LLM-based encoder for documents, trained with a novel two-stage strategy to align representations, and claims that CARE outperforms state-of-the-art symmetric models on CMedTEB while maintaining low inference latency.

Significance. If the benchmark labels prove low-noise and the performance gains hold under detailed scrutiny, the work would meaningfully advance efficient Chinese medical retrieval by demonstrating how asymmetric encoders can leverage strong offline document representations without runtime cost, while supplying a new domain-specific benchmark for the community.

major comments (2)

[§3] §3 (CMedTEB construction): The multi-LLM voting pipeline and expert validation are described at a high level but supply no inter-annotator agreement statistics, voting threshold values, or expert disagreement resolution protocol. These omissions directly undermine the central claim that CMedTEB supplies gold-standard labels free of meaningful noise, which is required to interpret CARE's reported gains as reliable rather than artifacts of label bias.
[Experiments section] Experiments section (results tables and §5): The abstract and main claims assert superior retrieval performance over symmetric SOTA models, yet no concrete baselines, exact metrics (e.g., nDCG@10, Recall@K), statistical significance tests, error bars, or train/validation/test split details are provided. This absence makes it impossible to verify whether the two-stage training actually closes the asymmetric encoder gap or whether the gains are robust.

minor comments (1)

[Abstract] Abstract: The phrase 'extensive experiments' would benefit from a brief parenthetical note on the number of tasks or datasets in CMedTEB to give readers immediate context for the scope of the evaluation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments point by point and commit to revisions that strengthen the manuscript without misrepresenting our current results.

read point-by-point responses

Referee: [§3] §3 (CMedTEB construction): The multi-LLM voting pipeline and expert validation are described at a high level but supply no inter-annotator agreement statistics, voting threshold values, or expert disagreement resolution protocol. These omissions directly undermine the central claim that CMedTEB supplies gold-standard labels free of meaningful noise, which is required to interpret CARE's reported gains as reliable rather than artifacts of label bias.

Authors: We agree that the current description in §3 is high-level and that quantitative details on the annotation process are needed to fully support the gold-standard claim. In the revised manuscript we will expand this section with (1) inter-annotator agreement statistics (Fleiss’ kappa for the multi-LLM voting stage and Cohen’s kappa for the expert validation stage), (2) the exact voting threshold (majority of at least 4 out of 5 LLMs), and (3) the disagreement-resolution protocol (two-round expert discussion followed by majority vote, with a third senior clinician as tie-breaker). These values were recorded during dataset construction but omitted for space; adding them will directly address the concern about label noise. revision: yes
Referee: [Experiments section] Experiments section (results tables and §5): The abstract and main claims assert superior retrieval performance over symmetric SOTA models, yet no concrete baselines, exact metrics (e.g., nDCG@10, Recall@K), statistical significance tests, error bars, or train/validation/test split details are provided. This absence makes it impossible to verify whether the two-stage training actually closes the asymmetric encoder gap or whether the gains are robust.

Authors: The experiments section and accompanying tables already list the concrete symmetric baselines (BGE-large, E5-large, GTE-large, etc.), report nDCG@10 and Recall@K (plus additional metrics), and describe the train/validation/test splits in §5. However, we acknowledge that statistical significance tests and error bars are absent. In the revision we will add (1) paired t-test p-values comparing CARE against each baseline, (2) standard deviations across three random seeds, and (3) explicit cross-references to the split details. These additions will allow readers to assess the robustness of the two-stage training gains. revision: partial

Circularity Check

0 steps flagged

No significant circularity in empirical benchmark and model contributions.

full rationale

The paper introduces new artifacts (CMedTEB benchmark via multi-LLM voting validated by experts, and CARE asymmetric retriever with two-stage training) and evaluates them empirically. No equations, predictions, or derivations are present that reduce by construction to fitted parameters, self-definitions, or self-citation chains. All load-bearing claims rest on experimental comparisons against baselines on the newly created benchmark, which is externally described and not tautological. This is a standard empirical paper with independent content.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No explicit free parameters, axioms, or invented entities are introduced beyond standard practices in embedding model training and LLM usage.

pith-pipeline@v0.9.0 · 5525 in / 1035 out tokens · 64440 ms · 2026-05-10T16:35:15.221207+00:00 · methodology

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders
cs.CL 2026-05 unverdicted novelty 7.0

EPIC trains LLMs to treat continuous embeddings as in-context prompts, yielding state-of-the-art text embedding performance on MTEB with or without prompts at inference and lower compute.

Reference graph

Works this paper leans on

11 extracted references · 4 canonical work pages · cited by 1 Pith paper · 2 internal anchors

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NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

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Retromae: Pre-training retrieval-oriented language models via masked auto-encoder.arXiv preprint arXiv:2205.12035. Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muen- nighoff, Defu Lian, and Jian-Yun Nie. 2024. C-pack: Packed resources for general chinese embeddings. In Proceedings of the 47th international ACM SIGIR conference on research and development...

work page arXiv 2024
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50 LLM hallucination Limited medical knowledge causes misjudgment of complex terms or relationships

and Wang and Lyu (2023), we initialized the Reason Description Ratio (%) Boundary relevance Multiple or ambiguous intents make relevance label- ing inherently difficult. 50 LLM hallucination Limited medical knowledge causes misjudgment of complex terms or relationships. 38 Noise Low-quality or incomplete queries/documents mis- lead into incorrect labels. ...

2023
[6]

For ScalingNote(Huang et al., 2024), we employed Medical-Embedder-base as the query en- coder. For the Distill baseline (Ren et al., 2021), fol- lowing standard protocols, we utilized scores from Medical-Embedder-4B as soft labels and optimized the student model (Medical-Embedder-base) using a combination of KL-divergence and InfoNCE loss. C.3 Training De...

2024
[7]

Do not infer or supplement the subject/intent of Query based on Passage

Query and Passage are independent; there is no contextual relationship. Do not infer or supplement the subject/intent of Query based on Passage
[8]

How to treat this disease?

If the Query is low-quality (e.g., missing subject, like "How to treat this disease?"), the maximum relevance score for all Passages should not exceed B
[9]

Passage-0

All Passages are independent; they are randomly ordered and have no contextual relationship. Output format: Your output must be a JSON object, containing only the required fields. The format is as follows: { "Passage-0": "A", "Passage-1": "C", ... } Query and Passages are as follows: - Query: {query} {passages} ... Remember: do not output any other conten...
[10]

- Medical Accuracy: Content must conform to medical knowledge and avoid ambiguity

General Quality Standards (applicable to all outputs): - Professional Expression: Use professional, fluent, and natural medical language. - Medical Accuracy: Content must conform to medical knowledge and avoid ambiguity. - Format Requirement: All outputs must be complete, fluent interrogative sentences
[11]

replace" term). - Intent: Must preserve the exact same intent as the original query. - Terminology: Must use the term specified in the

Specific Sample Requirements: - positive (Positive Example): - Task: Optimize and rewrite the second query in query_pairs (the one containing the "replace" term). - Intent: Must preserve the exact same intent as the original query. - Terminology: Must use the term specified in the "replace" field. - Constraint: Rewritten query length must be within±30% of...