PromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain

PromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain
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Original Paper
Biomedical language understanding benchmarks are the driving forces for artificial intelligence applications with large language model (LLM) back-ends. However, most current benchmarks: (a) are limited to English which makes it challenging to replicate many of the successes in English for other languages, or (b) focus on knowledge probing of LLMs and neglect to evaluate how LLMs apply these knowledge to perform on a wide range of bio-medical tasks, or (c) have become a publicly available corpus and are leaked to LLMs during pre-training. To facilitate the research in medical LLMs, we re-build the Chinese Biomedical Language Understanding Evaluation (CBLUE) benchmark into a large scale prompt-tuning benchmark, PromptCBLUE. Our benchmark is a suitable test-bed and an online platform for evaluating Chinese LLMs' multi-task capabilities on a wide range bio-medical tasks including medical entity recognition, medical text classification, medical natural language inference, medical dialogue understanding and medical content/dialogue generation. To establish evaluation on these tasks, we have experimented and report the results with the current 9 Chinese LLMs fine-tuned with differtent fine-tuning techniques.

Summary Notes

Harnessing Chinese Medical Language Models: The Emergence of PromptCBLUE

In the fast-paced world of artificial intelligence, language models like GPT-4 are reshaping our interaction with information across various fields.
The medical sector, known for its complex jargon and nuanced communication, faces unique challenges, especially with languages like Chinese.
This brings us to PromptCBLUE, a groundbreaking benchmark designed for Chinese language models in the medical field.

The Challenge

Merging language technology with healthcare aims to revolutionize areas from medical records to diagnostics.
Yet, the need for precision in medicine, coupled with the complexities of the Chinese language, calls for specialized benchmarks.
Previous benchmarks have struggled to accurately assess models on tasks reflecting the true complexity of medical language, particularly in Chinese.

What is PromptCBLUE?

PromptCBLUE expands on CBLUE (Chinese Biomedical Language Understanding Evaluation) to provide a multi-task prompt tuning benchmark. It evaluates Chinese language models on medical tasks including:
  • Medical Entity Recognition: Identifying medical terms in text.
  • Medical Text Classification: Categorizing medical documents.
  • Medical Natural Language Inference: Understanding relations between medical texts.
  • Medical Dialogue Understanding: Interpreting medical dialogues.
This ensures models are tested on both general language understanding and specialized medical knowledge.

Importance of PromptCBLUE

PromptCBLUE is a leap forward for AI in healthcare, offering:
  • Targeted Evaluation: Specific metrics for each task provide detailed insights into model performance.
  • Real-World Relevance: The benchmark mirrors actual medical scenarios, verifying the utility of models in real settings.
  • Specialized Fine-Tuning: Insights from PromptCBLUE can direct domain-specific improvements, enhancing model reliability.

Challenges and Insights

Initial results reveal general-purpose models like GPT-4 struggle with medical Chinese texts, highlighting the need for fine-tuning with medical-specific data to improve performance in healthcare communication.

The Future

PromptCBLUE is a critical step toward optimizing language models for the medical field. Continuous benchmark expansion and model refinement will be key.
The goal is to develop models that comprehend medical language and significantly contribute to patient care and research.


The creation of PromptCBLUE was made possible by the collaboration of computer science and medical experts, whose insights have been crucial in addressing the medical community's needs with AI technology.


PromptCBLUE introduces a specialized approach to tackling the unique challenges of Chinese medical language processing.
It opens new avenues for AI applications in healthcare, promising significant advancements in patient care and medical research.
As this benchmark evolves, the potential of medical language models continues to grow, marking a promising future for the field.

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