TCMLLM/LingLan

LingLan Traditional Chinese Medicine
Benchmark Dataset

     

中文说明

5 evaluation domains  ·   13 benchmark tasks  ·   25,620 Full items  ·   5,200 Hard items

Load the data  ·   Tasks and counts  ·   Explore all results

LingLan is an expert-curated benchmark for evaluating large language models on Traditional Chinese Medicine knowledge and clinical reasoning across five domains and 13 benchmark tasks. This repository distributes the labelled data; evaluation code is maintained in the GitHub project.

The counts above treat each joint diagnostic and therapeutic reasoning case as one item. Its labels are stored in three files, giving 15 task configurations and 30 JSONL files, with 29,620 Full and 6,000 Hard file rows. Every record has id, task, and answer.

Hard is a subset of Full, not an independent train/test partition. Each Hard file contains 400 records with the same IDs and labels as Full. Do not concatenate the two sets as independent examples.

Load from Hugging Face

Install the dependencies:

python -m pip install datasets

Load one task and one set:

from datasets import load_dataset

full = load_dataset("TCMLLM/LingLan", name="fundamental_tcm_knowledge_multiple_choice", split="fullset")
hard = load_dataset("TCMLLM/LingLan", name="fundamental_tcm_knowledge_multiple_choice", split="hardset")
print(len(full), len(hard))  # 1919, 400
print(hard[0]["id"], hard[0]["answer"])

The README YAML maps each task configuration to its two JSONL files. No custom dataset loading script or trust_remote_code=True is required.

Choose a configuration from the table below. fullset and hardset are the split names. Always specify the task; different tasks have different answer schemas.

Tasks and counts

Configuration / task	Full	Hard
chinese_patent_medicine_knowledge_single_choice	1,998	400
chinese_patent_medicine_knowledge_fill_blank	2,000	400
chinese_patent_medicine_knowledge_multiple_choice	1,950	400
herbal_prescription_single_choice	2,000	400
treatment_method_single_choice	2,000	400
syndrome_single_choice	2,000	400
herbal_prescription	2,000	400
treatment_method	2,000	400
syndrome	2,000	400
fundamental_tcm_knowledge_single_choice	1,962	400
fundamental_tcm_knowledge_fill_blank	1,959	400
fundamental_tcm_knowledge_multiple_choice	1,919	400
classical_text_entity_extraction	2,000	400
clinical_record_entity_extraction	2,000	400
tcm_licensing_examination	1,832	400

Structure and fields

LingLan/
├── README.md
├── README_zh.md
├── task_configs.json
├── data_manifest.json
└── data/
    ├── README.md
    ├── README_zh.md
    ├── fullset/
    └── hardset/

See data/README.md for all fields, answer formats, ID relationships, and evaluation output conventions. task_configs.json mirrors the README's task/file mappings. data_manifest.json records the row count and SHA-256 checksum of each JSONL file. The Hugging Face and ModelScope distributions contain identical JSONL bytes copied from the GitHub source data.

Independent and joint diagnostic and therapeutic reasoning evaluation

Diagnostic and therapeutic reasoning supports generating syndrome, treatment, or prescription independently, or all three in one call. Join the three files by four-digit case ID for joint generation; the GitHub runner uses this workflow. See diagnostic and therapeutic reasoning documentation for fields, entry-point names, and scoring.

This example loads the three gold answers for one shared case:

from datasets import load_dataset

tasks = ["syndrome", "treatment_method", "herbal_prescription"]
components = {}
for task in tasks:
    rows = load_dataset("TCMLLM/LingLan", name=task, split="hardset")
    components[task] = {r["id"].rsplit("_", 1)[1]: r for r in rows}
case_id = next(iter(components["syndrome"]))
records = [components[task][case_id] for task in tasks]
assert len({r["病历文本"] for r in records}) == 1
print(records[0]["病历文本"])
print({task: components[task][case_id]["answer"] for task in tasks})

Data sources and license

Data are curated from de-identified clinical records, classical texts, official examination sources, and structured knowledge resources.

The LingLan dataset is released under the Creative Commons Attribution 4.0 International license. Please credit LingLan, its authors, and the data source, and cite the paper when using it. When sharing the data, retain the license link and indicate any changes. See data/LICENSE for the full terms.

Citation

@misc{hua2026linglan,
      title={LingLan: A Unified Benchmark for Evaluating Large Language Models in Traditional Chinese Medicine},
      author={Rui Hua and Yu Wei and Zixin Shu and Kai Chang and Dengying Yan and Jianan Xia and Zeyu Liu and Hui Zhu and Shujie Song and Mingzhong Xiao and Xiaodong Li and Dongmei Jia and Zhuye Gao and Yanyan Meng and Naixuan Zhao and Yu Fu and Haibin Yu and Benman Yu and Yuanyuan Chen and Fei Dong and Zhizhou Meng and Pengcheng Yang and Songxue Zhao and Lijuan Pei and Yunhui Hu and Kan Ding and Jiayuan Duan and Wenmao Yin and Yang Gu and Runshun Zhang and Qiang Zhu and Jian Yu and Jiansheng Li and Baoyan Liu and Wenjia Wang and Xuezhong Zhou},
      year={2026},
      eprint={2602.01779},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2602.01779},
}