@inproceedings{chen-etal-2026-shizhengpt,
title = "{S}hizhen{GPT}: Towards Multimodal {LLM}s for Traditional {C}hinese Medicine",
author = "Chen, Junying and
Cai, Zhenyang and
Liu, Zhiheng and
Yang, Yunjin and
Wang, Rongsheng and
Xiao, Qingying and
Feng, Xiangyi and
Su, Zhan and
Guo, Jing and
Wan, Xiang and
Yu, Guangjun and
Li, Haizhou and
Wang, Benyou",
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.2037/",
pages = "43353--43375",
ISBN = "979-8-89176-462-0",
abstract = "Despite the success of large language models (LLMs), their potential in **Traditional Chinese Medicine (TCM)** remains underexplored due to scarce high-quality data and the multimodal nature of TCM diagnostics, which involve looking, listening, smelling, and pulse-taking. These modalities challenge conventional LLMs. To address these challenges, we present **ShizhenGPT**, the first multimodal LLM tailored for TCM. To overcome data scarcity, we curate the largest TCM dataset to date, comprising 100GB+ of text and 200GB+ of multimodal data, including 1.2M images, 200 hours of audio, and physiological signals. ShizhenGPT is pretrained and instruction-tuned to achieve deep TCM knowledge and multimodal reasoning. For evaluation, we collect recent national TCM qualification exams and build a visual benchmark for Medicinal Recognition and Visual Diagnosis. Experiments demonstrate that ShizhenGPT outperforms comparable-scale LLMs and competes with larger proprietary models. Moreover, it leads in TCM visual understanding among existing multimodal LLMs and shows promising results across modalities such as sound, pulse, electronic-nose signals, and vision, paving the way toward more holistic multimodal perception in TCM. We hope that the datasets, models, and methods of this work will inspire further exploration in this field. Code, models, and released data are available at https://github.com/FreedomIntelligence/ShizhenGPT."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chen-etal-2026-shizhengpt">
<titleInfo>
<title>ShizhenGPT: Towards Multimodal LLMs for Traditional Chinese Medicine</title>
</titleInfo>
<name type="personal">
<namePart type="given">Junying</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhenyang</namePart>
<namePart type="family">Cai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhiheng</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yunjin</namePart>
<namePart type="family">Yang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rongsheng</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Qingying</namePart>
<namePart type="family">Xiao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiangyi</namePart>
<namePart type="family">Feng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhan</namePart>
<namePart type="family">Su</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jing</namePart>
<namePart type="family">Guo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiang</namePart>
<namePart type="family">Wan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Guangjun</namePart>
<namePart type="family">Yu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Haizhou</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Benyou</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: EMNLP 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hung-Yi</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunipa</namePart>
<namePart type="family">Dev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jessy</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Desmond</namePart>
<namePart type="family">Elliott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-462-0</identifier>
</relatedItem>
<abstract>Despite the success of large language models (LLMs), their potential in **Traditional Chinese Medicine (TCM)** remains underexplored due to scarce high-quality data and the multimodal nature of TCM diagnostics, which involve looking, listening, smelling, and pulse-taking. These modalities challenge conventional LLMs. To address these challenges, we present **ShizhenGPT**, the first multimodal LLM tailored for TCM. To overcome data scarcity, we curate the largest TCM dataset to date, comprising 100GB+ of text and 200GB+ of multimodal data, including 1.2M images, 200 hours of audio, and physiological signals. ShizhenGPT is pretrained and instruction-tuned to achieve deep TCM knowledge and multimodal reasoning. For evaluation, we collect recent national TCM qualification exams and build a visual benchmark for Medicinal Recognition and Visual Diagnosis. Experiments demonstrate that ShizhenGPT outperforms comparable-scale LLMs and competes with larger proprietary models. Moreover, it leads in TCM visual understanding among existing multimodal LLMs and shows promising results across modalities such as sound, pulse, electronic-nose signals, and vision, paving the way toward more holistic multimodal perception in TCM. We hope that the datasets, models, and methods of this work will inspire further exploration in this field. Code, models, and released data are available at https://github.com/FreedomIntelligence/ShizhenGPT.</abstract>
<identifier type="citekey">chen-etal-2026-shizhengpt</identifier>
<location>
<url>https://aclanthology.org/2026.findings-emnlp.2037/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>43353</start>
<end>43375</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T ShizhenGPT: Towards Multimodal LLMs for Traditional Chinese Medicine
%A Chen, Junying
%A Cai, Zhenyang
%A Liu, Zhiheng
%A Yang, Yunjin
%A Wang, Rongsheng
%A Xiao, Qingying
%A Feng, Xiangyi
%A Su, Zhan
%A Guo, Jing
%A Wan, Xiang
%A Yu, Guangjun
%A Li, Haizhou
%A Wang, Benyou
%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 chen-etal-2026-shizhengpt
%X Despite the success of large language models (LLMs), their potential in **Traditional Chinese Medicine (TCM)** remains underexplored due to scarce high-quality data and the multimodal nature of TCM diagnostics, which involve looking, listening, smelling, and pulse-taking. These modalities challenge conventional LLMs. To address these challenges, we present **ShizhenGPT**, the first multimodal LLM tailored for TCM. To overcome data scarcity, we curate the largest TCM dataset to date, comprising 100GB+ of text and 200GB+ of multimodal data, including 1.2M images, 200 hours of audio, and physiological signals. ShizhenGPT is pretrained and instruction-tuned to achieve deep TCM knowledge and multimodal reasoning. For evaluation, we collect recent national TCM qualification exams and build a visual benchmark for Medicinal Recognition and Visual Diagnosis. Experiments demonstrate that ShizhenGPT outperforms comparable-scale LLMs and competes with larger proprietary models. Moreover, it leads in TCM visual understanding among existing multimodal LLMs and shows promising results across modalities such as sound, pulse, electronic-nose signals, and vision, paving the way toward more holistic multimodal perception in TCM. We hope that the datasets, models, and methods of this work will inspire further exploration in this field. Code, models, and released data are available at https://github.com/FreedomIntelligence/ShizhenGPT.
%U https://aclanthology.org/2026.findings-emnlp.2037/
%P 43353-43375
Markdown (Informal)
[ShizhenGPT: Towards Multimodal LLMs for Traditional Chinese Medicine](https://aclanthology.org/2026.findings-emnlp.2037/) (Chen et al., Findings 2026)
ACL
- Junying Chen, Zhenyang Cai, Zhiheng Liu, Yunjin Yang, Rongsheng Wang, Qingying Xiao, Xiangyi Feng, Zhan Su, Jing Guo, Xiang Wan, Guangjun Yu, Haizhou Li, and Benyou Wang. 2026. ShizhenGPT: Towards Multimodal LLMs for Traditional Chinese Medicine. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 43353–43375, Budapest, Hungary. Association for Computational Linguistics.