CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark

TL;DR

CBLUE is the first comprehensive Chinese biomedical language understanding benchmark, providing diverse tasks and evaluation platform to advance AI research in Chinese medical NLP, revealing current models lag behind human performance.

Contribution

This paper introduces CBLUE, a new Chinese biomedical NLP benchmark with multiple tasks and an evaluation platform, filling a gap in non-English biomedical AI research.

Findings

Current models perform significantly worse than humans on CBLUE tasks.

The benchmark covers diverse biomedical NLP tasks including NER, information extraction, and classification.

Empirical results highlight the need for improved Chinese biomedical language models.

Abstract

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}.