Papers › CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei LI, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice. With the development of biomedical language understanding benchmarks, AI applications are widely used in the medical field. However, most benchmarks are limited to English, which makes it challenging to replicate many of the successes in English for other languages. To facilitate research in this direction, we collect real-world biomedical data and present the first Chinese Biomedical Language Understanding Evaluation (CBLUE) benchmark: a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification, and an associated online platform for model evaluation, comparison, and analysis. To establish evaluation on these tasks, we report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling. Our benchmark is released at \url{https://tianchi.aliyun.com/dataset/dataDetail?dataId=95414&lang=en-us}.
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Code
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Code Syntology ran Syntology
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Intent Classification | KUAKE-QIC | RoBERTa-wwm-ext-base | Accuracy | 85.5 | #1 of 1 | Archive leaderboard | report |
| Medical Relation Extraction | CMeIE | RoBERTa-wwm-ext-large | Micro F1 | 55.9 | #1 of 1 | Archive leaderboard | report |
| Named Entity Recognition (NER) | CMeEE | MacBERT-large | Micro F1 | 62.4 | #2 of 2 | Archive leaderboard | report |
| Natural Language Inference | KUAKE-QQR | BERT-base | Accuracy | 84.7 | #1 of 1 | Archive leaderboard | report |
| Natural Language Inference | KUAKE-QTR | MacBERT-large | Accuracy | 62.9 | #1 of 1 | Archive leaderboard | report |
| Semantic Similarity | CHIP-STS | MacBERT-large | Macro F1 | 85.6 | #1 of 1 | Archive leaderboard | report |
| Sentence Classification | CHIP-CTC | RoBERTa-large | Macro F1 | 70.9 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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