Papers with Chinese medical
Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources (2021.findings-acl)
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| Challenge: | MRC is a popular task in NLP, aiming to understand a passage and answer the relevant questions. |
| Approach: | They propose a multi-target machine learning task for the medical domain that predicts answers to medical questions and corresponding support sentences from medical information sources simultaneously. |
| Outcome: | The proposed model outperforms baselines by fusing context-aware and knowledge-awful token representations. |
CliMedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models in Clinical Scenarios (2024.emnlp-main)
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| Challenge: | Chinese medical large language models (LLMs) are underperforming on this benchmark, especially where medical reasoning and factual consistency are vital. |
| Approach: | They propose a benchmark with 14 expert-guided clinical scenarios to assess the medical ability of large language models across 7 pivot dimensions. |
| Outcome: | The proposed benchmark has been validated in several ways. |
ChiMST: A Chinese Medical Corpus for Word Segmentation and Medical Term Recognition (2022.lrec-1)
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| Challenge: | Chinese word segmentation and named entity recognition are important tasks in natural language processing. |
| Approach: | They develop a Chinese medical corpus annotated with Chinese word boundary and medical term information to address this problem. |
| Outcome: | The proposed corpus will be a valuable resource for Chinese word segmentation and named entity recognition research on the medical domain. |
KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition (2023.findings-acl)
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| Challenge: | Existing frameworks for symptom status recognition in doctor-patient dialogues are inadequate. |
| Approach: | They propose a framework for symptom status recognition that formalizes a natural language inference task . they generate knowledge about the symptom and a hypothesis about its status for each symptom . |
| Outcome: | The proposed framework outperforms baselines and has advantages in cross-disease and cross-symptom scenarios. |
CAMEC: Complexity-Aware Multi-Expert Collaboration for Reliable Chinese Medical Question Answering (2026.acl-long)
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| Challenge: | Large language models are promising for medical question answering in china, but remain unreliable due to hallucinations, weak factual grounding and difficulty handling clinically complex cases. |
| Approach: | They propose a framework that combines hierarchical medical adaptation with complexity-aware expert routing for reliable Chinese medical QA. |
| Outcome: | The proposed framework outperforms strong general and medical LLM baselines on four Chinese medical benchmarks. |