@inproceedings{zheng-etal-2026-clinconsensus,
title = "{C}lin{C}onsensus: A Physician-Calibrated Benchmark for Evaluating Clinical Rubric Coverage in {C}hinese Medical {LLM}s",
author = "Zheng, Xiang and
Li, Han and
Luo, Wenjie and
Zhai, Weiqi and
Li, Yiyuan and
Yan, Chuanmiao and
Yang, Xue and
Wu, Kailuan and
Xu, Ruiyi and
Lu, Tianyun and
Tang, Tianyi and
Ma, Yubo and
Yang, Kexin and
Yang, Sen and
Liu, Dayiheng and
Qu, Lin and
Zhao, Bing and
Wei, HU",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1339/",
pages = "28381--28405",
ISBN = "979-8-89176-462-0",
abstract = "Open-ended medical LLM evaluation remains weakly grounded in physician-calibrated coverage of clinically relevant response criteria, especially in localized clinical settings. We introduce ClinConsensus, a Chinese medical benchmark of 2,500 expert-curated cases spanning 36 specialties, 12 task themes, multiple difficulty levels, and lay-facing versus professional-facing settings. Each case is paired with 30 case-specific binary rubric criteria. To evaluate whether responses satisfy enough physician-authored criteria, we propose Clinician-Anchored Coverage Score (CACS), a physician-calibrated threshold metric instantiated at k = 10, and develop a dual-judge framework combining a GPT-5.1 grader with a physician-supervised Qwen3-8B judge. Evaluating 11 frontier LLMs, we find a persistent coverage gap: Rubric Accuracy ranges from 39.6{\%} to 52.1{\%}, whereas CACS@10 ranges from 17.8{\%} to 32.9{\%}, leaving a 19.2{--}21.9 point gap across models. Stratified analyses further reveal substantial variation across reasoning, evidence use, structured extraction, medication instructions, follow-up, and dialogue register. These results suggest that medical LLM evaluation should measure thresholded, rubric-grounded clinical coverage rather than average partial correctness."
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<abstract>Open-ended medical LLM evaluation remains weakly grounded in physician-calibrated coverage of clinically relevant response criteria, especially in localized clinical settings. We introduce ClinConsensus, a Chinese medical benchmark of 2,500 expert-curated cases spanning 36 specialties, 12 task themes, multiple difficulty levels, and lay-facing versus professional-facing settings. Each case is paired with 30 case-specific binary rubric criteria. To evaluate whether responses satisfy enough physician-authored criteria, we propose Clinician-Anchored Coverage Score (CACS), a physician-calibrated threshold metric instantiated at k = 10, and develop a dual-judge framework combining a GPT-5.1 grader with a physician-supervised Qwen3-8B judge. Evaluating 11 frontier LLMs, we find a persistent coverage gap: Rubric Accuracy ranges from 39.6% to 52.1%, whereas CACS@10 ranges from 17.8% to 32.9%, leaving a 19.2–21.9 point gap across models. Stratified analyses further reveal substantial variation across reasoning, evidence use, structured extraction, medication instructions, follow-up, and dialogue register. These results suggest that medical LLM evaluation should measure thresholded, rubric-grounded clinical coverage rather than average partial correctness.</abstract>
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%0 Conference Proceedings
%T ClinConsensus: A Physician-Calibrated Benchmark for Evaluating Clinical Rubric Coverage in Chinese Medical LLMs
%A Zheng, Xiang
%A Li, Han
%A Luo, Wenjie
%A Zhai, Weiqi
%A Li, Yiyuan
%A Yan, Chuanmiao
%A Yang, Xue
%A Wu, Kailuan
%A Xu, Ruiyi
%A Lu, Tianyun
%A Tang, Tianyi
%A Ma, Yubo
%A Yang, Kexin
%A Yang, Sen
%A Liu, Dayiheng
%A Qu, Lin
%A Zhao, Bing
%A Wei, H. U.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zheng-etal-2026-clinconsensus
%X Open-ended medical LLM evaluation remains weakly grounded in physician-calibrated coverage of clinically relevant response criteria, especially in localized clinical settings. We introduce ClinConsensus, a Chinese medical benchmark of 2,500 expert-curated cases spanning 36 specialties, 12 task themes, multiple difficulty levels, and lay-facing versus professional-facing settings. Each case is paired with 30 case-specific binary rubric criteria. To evaluate whether responses satisfy enough physician-authored criteria, we propose Clinician-Anchored Coverage Score (CACS), a physician-calibrated threshold metric instantiated at k = 10, and develop a dual-judge framework combining a GPT-5.1 grader with a physician-supervised Qwen3-8B judge. Evaluating 11 frontier LLMs, we find a persistent coverage gap: Rubric Accuracy ranges from 39.6% to 52.1%, whereas CACS@10 ranges from 17.8% to 32.9%, leaving a 19.2–21.9 point gap across models. Stratified analyses further reveal substantial variation across reasoning, evidence use, structured extraction, medication instructions, follow-up, and dialogue register. These results suggest that medical LLM evaluation should measure thresholded, rubric-grounded clinical coverage rather than average partial correctness.
%U https://aclanthology.org/2026.findings-emnlp.1339/
%P 28381-28405
Markdown (Informal)
[ClinConsensus: A Physician-Calibrated Benchmark for Evaluating Clinical Rubric Coverage in Chinese Medical LLMs](https://aclanthology.org/2026.findings-emnlp.1339/) (Zheng et al., Findings 2026)
ACL
- Xiang Zheng, Han Li, Wenjie Luo, Weiqi Zhai, Yiyuan Li, Chuanmiao Yan, Xue Yang, Kailuan Wu, Ruiyi Xu, Tianyun Lu, Tianyi Tang, Yubo Ma, Kexin Yang, Sen Yang, Dayiheng Liu, Lin Qu, Bing Zhao, and HU Wei. 2026. ClinConsensus: A Physician-Calibrated Benchmark for Evaluating Clinical Rubric Coverage in Chinese Medical LLMs. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 28381–28405, Budapest, Hungary. Association for Computational Linguistics.