---
title: "HuatuoGPT, Towards Taming Language Model to Be — Zhang 2023"
description: "The paper tackles how to build a Chinese medical consultation model that is both clear for patients and clinically competent."
authors: ["Hongbo Zhang", "Junying Chen", "Feng Jiang", "Fei Yu", "Zhihong Chen", "Jianquan Li", "Guiming Chen", "Xiangbo Wu", "Zhiyi Zhang", "Qingying Xiao", "Xiang Wan", "Benyou Wang", "Haizhou Li"]
year: 2023
venue: "Findings of the Association for Computational Linguistics: EMNLP 2023"
source: "https://aclanthology.org/2023.findings-emnlp.725/"
doi: "10.18653/v1/2023.findings-emnlp.725"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2023-huatuogpt-taming-language-model-be/"
---
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# HuatuoGPT, Towards Taming Language Model to Be a Doctor

**Authors:** Hongbo Zhang, [Junying Chen](https://md.research.elamur.ai/free/en/authors/junying-chen/), [Feng Jiang](https://md.research.elamur.ai/free/en/authors/feng-jiang/), [Fei Yu](https://md.research.elamur.ai/free/en/authors/fei-yu/), [Zhihong Chen](https://md.research.elamur.ai/free/en/authors/zhihong-chen/), Jianquan Li, Guiming Chen, Xiangbo Wu, Zhiyi Zhang, Qingying Xiao, Xiang Wan, [Benyou Wang](https://md.research.elamur.ai/free/en/authors/benyou-wang/) et al. (13 authors) · **Year:** 2023 · **Venue:** Findings of the Association for Computational Linguistics: EMNLP 2023 · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.findings-emnlp.725/) · [PDF](https://aclanthology.org/2023.findings-emnlp.725.pdf) · [DOI 10.18653/v1/2023.findings-emnlp.725](https://doi.org/10.18653/v1/2023.findings-emnlp.725) · [OpenAlex](https://openalex.org/W4389520259)

**Citations:** 206 citations (openalex-snapshot · 2026-10-14) · [73](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper tackles how to build a Chinese medical consultation model that is both clear for patients and clinically competent. The authors argue that each training source alone falls short. Models distilled from ChatGPT produce fluent, well-organized answers but rarely ask follow-up questions or commit to a diagnosis. Models trained only on real doctor–patient dialogues inherit doctors' terse, sometimes incoherent replies. HuatuoGPT addresses this in two stages. First, supervised fine-tuning uses a mix of ChatGPT-generated instructions and conversations plus publicly available real-world doctor data, with the two sources brought closer through role-enhanced prompting and ChatGPT polishing. Second, Reinforcement Learning from Mixed Feedback (RLMF) trains a reward model that favours answers combining patient-friendly presentation with doctor-like, interactive diagnosis. GPT-4 judging, doctor assessment and medical benchmarks show HuatuoGPT ahead of open-source medical LLMs. In most settings it also beats its teacher, GPT-3.5-turbo. The work suggests that grounding distillation in expert data can push a student model past its teacher in a specialized domain.

## Key points
- Real-world training data comes from public sources only: single-turn Q&A from Huatuo26M and multi-turn dialogues from Med-dialog. Distilled data was built with self-instruct, starting from a top-down taxonomy of roles and use cases.
- RLMF is modelled on RLHF and RLAIF, but its reward signal reflects both ChatGPT-style quality and doctor-style professionalism.
- HuatuoGPT reports state-of-the-art zero-shot results on CmedQA, webmedQA and Huatuo26M.
- Human evaluation involved three paid doctors, one of them a chief physician. A Bloom-7B1-mt variant was trained for internal comparison but will not be released.
- The authors plan to release the training data, code, model and reward model (FreedomIntelligence/HuatuoGPT on GitHub). Knowledge-intensive multiple-choice benchmarking was left for future work.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

73 papers of our library cite it (of the 140884 whose bibliographies we read); 5 of them have a page here, 68 not yet.

- [AceGPT, Localizing Large Language Models in Arabic](https://md.research.elamur.ai/free/en/papers/huang-2024-acegpt-localizing-large-language-models/)
- [从基础智能到通用智能: 基于大模型的GenAI和AGI之现状与展望](https://md.research.elamur.ai/free/en/papers/miao-2024-foundation-intelligence-general/)
- [Large Language Models for Data Annotation and Synthesis: A Survey](https://md.research.elamur.ai/free/en/papers/tan-2024-large-language-models-data-annotation/)
- [Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhang2023huatuogpt,
  title = {{HuatuoGPT, Towards Taming Language Model to Be a Doctor}},
  author = {Zhang, Hongbo and Chen, Junying and Jiang, Feng and Yu, Fei and Chen, Zhihong and Li, Jianquan and Chen, Guiming and Wu, Xiangbo and Zhang, Zhiyi and Xiao, Qingying and Wan, Xiang and Wang, Benyou and Li, Haizhou},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023},
  year = {2023},
  doi = {10.18653/v1/2023.findings-emnlp.725},
  url = {https://doi.org/10.18653/v1/2023.findings-emnlp.725}
}
```

- [← previous HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge](https://md.research.elamur.ai/free/en/papers/wang-2023-huatuo-tuning-llama-model-chinese/)
- [next → HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://md.research.elamur.ai/free/en/papers/shen-2023-hugginggpt-solving-ai-tasks-chatgpt/)
- [↑ back to papers of 2023](https://md.research.elamur.ai/free/en/papers/2023/page/4/)

## References

33 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. 2022. Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073. [find](https://scholar.google.com/scholar?q=Yuntao+Bai%2C+Saurav+Kadavath%2C+Sandipan+Kundu%2C+Amanda+Askell%2C+Jackson+Kernion%2C+Andy+Jones%2C+Anna+Chen%2C+Anna+Goldie%2C+Azalia+Mirhoseini%2C+Cameron+McKinnon%2C+et+al%2E+2022%2E+Constitutional+ai%3A+Harmlessness+from+) [our card](https://md.research.elamur.ai/free/en/papers/bai-2022-constitutional-ai-harmlessness-ai-feedback/)
2. Zhihong Chen, Feng Jiang, Junying Chen, Tiannan Wang, Fei Yu, Guiming Chen, Hongbo Zhang, Juhao Liang, Chen Zhang, Zhiyi Zhang, et al. 2023. Phoenix: Democratizing chatgpt across languages. arXiv preprint arXiv:2304.10453. [find](https://scholar.google.com/scholar?q=Zhihong+Chen%2C+Feng+Jiang%2C+Junying+Chen%2C+Tiannan+Wang%2C+Fei+Yu%2C+Guiming+Chen%2C+Hongbo+Zhang%2C+Juhao+Liang%2C+Chen+Zhang%2C+Zhiyi+Zhang%2C+et+al%2E+2023%2E+Phoenix%3A+Democratizing+chatgpt+across+languages%2E+arXiv+prep)
3. Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. [find](https://scholar.google.com/scholar?q=Wei%2DLin+Chiang%2C+Zhuohan+Li%2C+Zi+Lin%2C+Ying+Sheng%2C+Zhanghao+Wu%2C+Hao+Zhang%2C+Lianmin+Zheng%2C+Siyuan+Zhuang%2C+Yonghao+Zhuang%2C+Joseph+E%2E+Gonzalez%2C+Ion+Stoica%2C+and+Eric+P%2E+Xing%2E+2023%2E+Vicuna%3A+An+open%2Dsource+cha)
4. Tianyu Han, Lisa C. Adams, Jens-Michalis Papaioannou, Paul Grundmann, Tom Oberhauser, Alexander Löser, Daniel Truhn, and Keno K. Bressem. 2023. Medalpaca – an open-source collection of medical conversational ai models and training data. [find](https://scholar.google.com/scholar?q=Tianyu+Han%2C+Lisa+C%2E+Adams%2C+Jens%2DMichalis+Papaioannou%2C+Paul+Grundmann%2C+Tom+Oberhauser%2C+Alexander+L%C3%B6ser%2C+Daniel+Truhn%2C+and+Keno+K%2E+Bressem%2E+2023%2E+Medalpaca+%E2%80%93+an+open%2Dsource+collection+of+medical+convers) [our card](https://md.research.elamur.ai/free/en/papers/han-2023-medalpaca-open-source-collection-medical/)
5. Junqing He, Mingming Fu, and Manshu Tu. 2019. Applying deep matching networks to chinese medical question answering: a study and a dataset. BMC medical informatics and decision making, 19(2):91–100. [find](https://scholar.google.com/scholar?q=Junqing+He%2C+Mingming+Fu%2C+and+Manshu+Tu%2E+2019%2E+Applying+deep+matching+networks+to+chinese+medical+question+answering%3A+a+study+and+a+dataset%2E+BMC+medical+informatics+and+decision+making%2C+19%282%29%3A91%E2%80%93100%2E)
6. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021. Measuring massive multitask language understanding. Proceedings of the International Conference on Learning Representations (ICLR). [find](https://scholar.google.com/scholar?q=Dan+Hendrycks%2C+Collin+Burns%2C+Steven+Basart%2C+Andy+Zou%2C+Mantas+Mazeika%2C+Dawn+Song%2C+and+Jacob+Steinhardt%2E+2021%2E+Measuring+massive+multitask+language+understanding%2E+Proceedings+of+the+International+Confer) [our card](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/)
7. Yuzhen Huang, Yuzhuo Bai, Zhihao Zhu, Junlei Zhang, Jinghan Zhang, Tangjun Su, Junteng Liu, Chuancheng Lv, Yikai Zhang, Jiayi Lei, Yao Fu, Maosong Sun, and Junxian He. 2023. Ceval: A multi-level multi-discipline chinese evaluation suite for foundation models. arXiv preprint arXiv:2305.08322. [find](https://scholar.google.com/scholar?q=Yuzhen+Huang%2C+Yuzhuo+Bai%2C+Zhihao+Zhu%2C+Junlei+Zhang%2C+Jinghan+Zhang%2C+Tangjun+Su%2C+Junteng+Liu%2C+Chuancheng+Lv%2C+Yikai+Zhang%2C+Jiayi+Lei%2C+Yao+Fu%2C+Maosong+Sun%2C+and+Junxian+He%2E+2023%2E+Ceval%3A+A+multi%2Dlevel+multi) [our card](https://md.research.elamur.ai/free/en/papers/huang-2023-c-eval-multi-level-multi/)
8. Jianquan Li, Xidong Wang, Xiangbo Wu, Zhiyi Zhang, Xiaolong Xu, Jie Fu, Prayag Tiwari, Xiang Wan, and Benyou Wang. 2023a. Huatuo-26m, a large-scale chinese medical qa dataset. arXiv preprint arXiv:2305.01526. [find](https://scholar.google.com/scholar?q=Jianquan+Li%2C+Xidong+Wang%2C+Xiangbo+Wu%2C+Zhiyi+Zhang%2C+Xiaolong+Xu%2C+Jie+Fu%2C+Prayag+Tiwari%2C+Xiang+Wan%2C+and+Benyou+Wang%2E+2023a%2E+Huatuo%2D26m%2C+a+large%2Dscale+chinese+medical+qa+dataset%2E+arXiv+preprint+arXiv%3A230)
9. Xuechen Li, Tianyi Zhang, Yann Dubois, Rohan Taori, Ishaan Gulrajani, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023b. Alpacaeval: An automatic evaluator of instruction-following models. [https://github.com/tatsu-lab/alpaca_eval](https://github.com/tatsu-lab/alpaca_eval). [↗](https://github.com/tatsu-lab/alpaca_eval)
10. Yunxiang Li, Zihan Li, Kai Zhang, Ruilong Dan, and You Zhang. 2023c. Chatdoctor: A medical chat model fine-tuned on llama model using medical domain knowledge. [find](https://scholar.google.com/scholar?q=Yunxiang+Li%2C+Zihan+Li%2C+Kai+Zhang%2C+Ruilong+Dan%2C+and+You+Zhang%2E+2023c%2E+Chatdoctor%3A+A+medical+chat+model+fine%2Dtuned+on+llama+model+using+medical+domain+knowledge%2E)
11. Hongcheng Liu, Yusheng Liao, Yutong Meng, Yu Wang, and Yanfeng Wang. 2023. Medicalgpt-zh： 中 文 医 疗 对 话 语 言 模 型. [https://github.com/](https://github.com/) MediaBrain-SJTU/MedicalGPT-zh. [↗](https://github.com/)
12. Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon, and Tie-Yan Liu. 2022. BioGPT: generative pre-trained transformer for biomedical text generation and mining,. 23(6). [find](https://scholar.google.com/scholar?q=Renqian+Luo%2C+Liai+Sun%2C+Yingce+Xia%2C+Tao+Qin%2C+Sheng+Zhang%2C+Hoifung+Poon%2C+and+Tie%2DYan+Liu%2E+2022%2E+BioGPT%3A+generative+pre%2Dtrained+transformer+for+biomedical+text+generation+and+mining%2C%2E+23%286%29%2E)
13. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35:27730–27744. [find](https://scholar.google.com/scholar?q=Long+Ouyang%2C+Jeffrey+Wu%2C+Xu+Jiang%2C+Diogo+Almeida%2C+Carroll+Wainwright%2C+Pamela+Mishkin%2C+Chong+Zhang%2C+Sandhini+Agarwal%2C+Katarina+Slama%2C+Alex+Ray%2C+et+al%2E+2022%2E+Training+language+models+to+follow+instructi)
14. Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao. 2023. Instruction tuning with gpt-4. arXiv preprint arXiv:2304.03277. [find](https://scholar.google.com/scholar?q=Baolin+Peng%2C+Chunyuan+Li%2C+Pengcheng+He%2C+Michel+Galley%2C+and+Jianfeng+Gao%2E+2023%2E+Instruction+tuning+with+gpt%2D4%2E+arXiv+preprint+arXiv%3A2304%2E03277%2E) [our card](https://md.research.elamur.ai/free/en/papers/peng-2023-instruction-tuning-gpt-4/)
15. Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He. 2020. Zero: Memory optimizations toward training trillion parameter models. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1– 16. IEEE. [find](https://scholar.google.com/scholar?q=Samyam+Rajbhandari%2C+Jeff+Rasley%2C+Olatunji+Ruwase%2C+and+Yuxiong+He%2E+2020%2E+Zero%3A+Memory+optimizations+toward+training+trillion+parameter+models%2E+In+SC20%3A+International+Conference+for+High+Performance+Com)
16. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347. [find](https://scholar.google.com/scholar?q=John+Schulman%2C+Filip+Wolski%2C+Prafulla+Dhariwal%2C+Alec+Radford%2C+and+Oleg+Klimov%2E+2017%2E+Proximal+policy+optimization+algorithms%2E+arXiv+preprint+arXiv%3A1707%2E06347%2E)
17. Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, and Ross Anderson. 2023. The curse of recursion: Training on generated data makes models forget. [find](https://scholar.google.com/scholar?q=Ilia+Shumailov%2C+Zakhar+Shumaylov%2C+Yiren+Zhao%2C+Yarin+Gal%2C+Nicolas+Papernot%2C+and+Ross+Anderson%2E+2023%2E+The+curse+of+recursion%3A+Training+on+generated+data+makes+models+forget%2E) [our card](https://md.research.elamur.ai/free/en/papers/shumailov-2023-curse-recursion-training-generated-data/)
18. Karan Singhal, Tao Tu, Juraj Gottweis, Rory Sayres, Ellery Wulczyn, Le Hou, Kevin Clark, Stephen Pfohl, Heather Cole-Lewis, Darlene Neal, Mike Schaekermann, Amy Wang, Mohamed Amin, Sami Lachgar, Philip Mansfield, Sushant Prakash, Bradley Green, Ewa Dominowska, Blaise Aguera y Arcas, Nenad Tomasev, Y… [find](https://scholar.google.com/scholar?q=Karan+Singhal%2C+Tao+Tu%2C+Juraj+Gottweis%2C+Rory+Sayres%2C+Ellery+Wulczyn%2C+Le+Hou%2C+Kevin+Clark%2C+Stephen+Pfohl%2C+Heather+Cole%2DLewis%2C+Darlene+Neal%2C+Mike+Schaekermann%2C+Amy+Wang%2C+Mohamed+Amin%2C+Sami+Lachgar%2C+Phili) [our card](https://md.research.elamur.ai/free/en/papers/singhal-2023-towards-expert-level-medical-question/)
19. Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford alpaca: An instruction-following llama model. [https://github.com/tatsu-lab/stanford_alpaca](https://github.com/tatsu-lab/stanford_alpaca). [↗](https://github.com/tatsu-lab/stanford_alpaca)
20. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. [find](https://scholar.google.com/scholar?q=Hugo+Touvron%2C+Thibaut+Lavril%2C+Gautier+Izacard%2C+Xavier+Martinet%2C+Marie%2DAnne+Lachaux%2C+Timoth%C3%A9e+Lacroix%2C+Baptiste+Rozi%C3%A8re%2C+Naman+Goyal%2C+Eric+Hambro%2C+Faisal+Azhar%2C+Aurelien+Rodriguez%2C+Armand+Joulin%2C+Edoua)
21. Benyou Wang, Qianqian Xie, Jiahuan Pei, Zhihong Chen, Prayag Tiwari, Zhao Li, and Jie Fu. 2023a. Pre-trained language models in biomedical domain: A systematic survey. ACM Computing Surveys, 56(3):1– 52. [find](https://scholar.google.com/scholar?q=Benyou+Wang%2C+Qianqian+Xie%2C+Jiahuan+Pei%2C+Zhihong+Chen%2C+Prayag+Tiwari%2C+Zhao+Li%2C+and+Jie+Fu%2E+2023a%2E+Pre%2Dtrained+language+models+in+biomedical+domain%3A+A+systematic+survey%2E+ACM+Computing+Surveys%2C+56%283%29%3A1%E2%80%93+)
22. Haochun Wang, Chi Liu, Nuwa Xi, Zewen Qiang, Sendong Zhao, Bing Qin, and Ting Liu. 2023b. Huatuo: Tuning llama model with chinese medical knowledge. [find](https://scholar.google.com/scholar?q=Haochun+Wang%2C+Chi+Liu%2C+Nuwa+Xi%2C+Zewen+Qiang%2C+Sendong+Zhao%2C+Bing+Qin%2C+and+Ting+Liu%2E+2023b%2E+Huatuo%3A+Tuning+llama+model+with+chinese+medical+knowledge%2E) [our card](https://md.research.elamur.ai/free/en/papers/wang-2023-huatuo-tuning-llama-model-chinese/)
23. Peiyi Wang, Lei Li, Liang Chen, Dawei Zhu, Binghuai Lin, Yunbo Cao, Qi Liu, Tianyu Liu, and Zhifang Sui. 2023c. Large language models are not fair evaluators. arXiv preprint arXiv:2305.17926. [find](https://scholar.google.com/scholar?q=Peiyi+Wang%2C+Lei+Li%2C+Liang+Chen%2C+Dawei+Zhu%2C+Binghuai+Lin%2C+Yunbo+Cao%2C+Qi+Liu%2C+Tianyu+Liu%2C+and+Zhifang+Sui%2E+2023c%2E+Large+language+models+are+not+fair+evaluators%2E+arXiv+preprint+arXiv%3A2305%2E17926%2E)
24. Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2022. Self-instruct: Aligning language model with self generated instructions. arXiv preprint arXiv:2212.10560. [find](https://scholar.google.com/scholar?q=Yizhong+Wang%2C+Yeganeh+Kordi%2C+Swaroop+Mishra%2C+Alisa+Liu%2C+Noah+A+Smith%2C+Daniel+Khashabi%2C+and+Hannaneh+Hajishirzi%2E+2022%2E+Self%2Dinstruct%3A+Aligning+language+model+with+self+generated+instructions%2E+arXiv+pre)
25. Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, et al. 2020. Meddialog: Large-scale medical dialogue datasets. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9241–… [find](https://scholar.google.com/scholar?q=Guangtao+Zeng%2C+Wenmian+Yang%2C+Zeqian+Ju%2C+Yue+Yang%2C+Sicheng+Wang%2C+Ruisi+Zhang%2C+Meng+Zhou%2C+Jiaqi+Zeng%2C+Xiangyu+Dong%2C+Ruoyu+Zhang%2C+et+al%2E+2020%2E+Meddialog%3A+Large%2Dscale+medical+dialogue+datasets%2E+In+Proceed)
26. Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, et al. 2021. Cblue: A chinese biomedical language understanding evaluation benchmark. arXiv preprint arXiv:2106.08087. [find](https://scholar.google.com/scholar?q=Ningyu+Zhang%2C+Mosha+Chen%2C+Zhen+Bi%2C+Xiaozhuan+Liang%2C+Lei+Li%2C+Xin+Shang%2C+Kangping+Yin%2C+Chuanqi+Tan%2C+Jian+Xu%2C+Fei+Huang%2C+et+al%2E+2021%2E+Cblue%3A+A+chinese+biomedical+language+understanding+evaluation+benchma)
27. S. Zhang, X. Zhang, H. Wang, L. Guo, and S. Liu. 2018. Multi-scale attentive interaction networks for chinese medical question answer selection. IEEE Access, 6:74061–74071. [find](https://scholar.google.com/scholar?q=S%2E+Zhang%2C+X%2E+Zhang%2C+H%2E+Wang%2C+L%2E+Guo%2C+and+S%2E+Liu%2E+2018%2E+Multi%2Dscale+attentive+interaction+networks+for+chinese+medical+question+answer+selection%2E+IEEE+Access%2C+6%3A74061%E2%80%9374071%2E)
28. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. Judging llm-as-a-judge with mt-bench and chatbot arena. [find](https://scholar.google.com/scholar?q=Lianmin+Zheng%2C+Wei%2DLin+Chiang%2C+Ying+Sheng%2C+Siyuan+Zhuang%2C+Zhanghao+Wu%2C+Yonghao+Zhuang%2C+Zi+Lin%2C+Zhuohan+Li%2C+Dacheng+Li%2C+Eric%2E+P+Xing%2C+Hao+Zhang%2C+Joseph+E%2E+Gonzalez%2C+and+Ion+Stoica%2E+2023%2E+Judging+llm%2Das)
29. Zhongyu Wei, Qianlong Liu, Baolin Peng, Huaixiao Tou, Ting Chen, Xuan-Jing Huang, Kam-Fai Wong, and Xiang Dai. 2018. Task-oriented dialogue system for automatic diagnosis. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 201–… [find](https://scholar.google.com/scholar?q=Zhongyu+Wei%2C+Qianlong+Liu%2C+Baolin+Peng%2C+Huaixiao+Tou%2C+Ting+Chen%2C+Xuan%2DJing+Huang%2C+Kam%2DFai+Wong%2C+and+Xiang+Dai%2E+2018%2E+Task%2Doriented+dialogue+system+for+automatic+diagnosis%2E+In+Proceedings+of+the+56th+A)
30. Yuan Xia, Jingbo Zhou, Zhenhui Shi, Chao Lu, and Haifeng Huang. 2020. Generative adversarial regularized mutual information policy gradient framework for automatic diagnosis. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 1062–1069. [find](https://scholar.google.com/scholar?q=Yuan+Xia%2C+Jingbo+Zhou%2C+Zhenhui+Shi%2C+Chao+Lu%2C+and+Haifeng+Huang%2E+2020%2E+Generative+adversarial+regularized+mutual+information+policy+gradient+framework+for+automatic+diagnosis%2E+In+Proceedings+of+the+AAA)
31. Honglin Xiong, Sheng Wang, Yitao Zhu, Zihao Zhao, Yuxiao Liu, Qian Wang, and Dinggang Shen. 2023. Doctorglm: Fine-tuning your chinese doctor is not a herculean task. arXiv preprint arXiv:2304.01097. [find](https://scholar.google.com/scholar?q=Honglin+Xiong%2C+Sheng+Wang%2C+Yitao+Zhu%2C+Zihao+Zhao%2C+Yuxiao+Liu%2C+Qian+Wang%2C+and+Dinggang+Shen%2E+2023%2E+Doctorglm%3A+Fine%2Dtuning+your+chinese+doctor+is+not+a+herculean+task%2E+arXiv+preprint+arXiv%3A2304%2E01097%2E)
32. Lin Xu, Qixian Zhou, Ke Gong, Xiaodan Liang, Jianheng Tang, and Liang Lin. 2019. End-to-end knowledge-routed relational dialogue system for automatic diagnosis. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 7346–7353. [find](https://scholar.google.com/scholar?q=Lin+Xu%2C+Qixian+Zhou%2C+Ke+Gong%2C+Xiaodan+Liang%2C+Jianheng+Tang%2C+and+Liang+Lin%2E+2019%2E+End%2Dto%2Dend+knowledge%2Drouted+relational+dialogue+system+for+automatic+diagnosis%2E+In+Proceedings+of+the+AAAI+conference+o)
33. Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, Weng Lam Tam, Zixuan Ma, Yufei Xue, Jidong Zhai, Wenguang Chen, Zhiyuan Liu, Peng Zhang, Yuxiao Dong, and Jie Tang. 2023. GLM-130b: An open bilingual pre-trained model. In The Eleventh… [find](https://scholar.google.com/scholar?q=Aohan+Zeng%2C+Xiao+Liu%2C+Zhengxiao+Du%2C+Zihan+Wang%2C+Hanyu+Lai%2C+Ming+Ding%2C+Zhuoyi+Yang%2C+Yifan+Xu%2C+Wendi+Zheng%2C+Xiao+Xia%2C+Weng+Lam+Tam%2C+Zixuan+Ma%2C+Yufei+Xue%2C+Jidong+Zhai%2C+Wenguang+Chen%2C+Zhiyuan+Liu%2C+Peng+Zh) [our card](https://md.research.elamur.ai/free/en/papers/zeng-2022-glm-130b-open-bilingual-pre/)