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cs.CL2025
AutoMedEval: Harnessing Language Models for Automatic Medical Capability Evaluation
Xiechi Zhang, Zetian Ouyang, Linlin Wang +6
With the proliferation of large language models (LLMs) in the medical domain, there is increasing demand for improved evaluation techniques to assess their capabilities. However, t…
cs.CL2024★ 1 cited
CliMedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models in Clinical Scenarios
Zetian Ouyang, Yishuai Qiu, Linlin Wang +4
With the proliferation of Large Language Models (LLMs) in diverse domains, there is a particular need for unified evaluation standards in clinical medical scenarios, where models n…
cs.CL2024★ 1 cited
Towards Evaluating and Building Versatile Large Language Models for Medicine
Chaoyi Wu, Pengcheng Qiu, Jinxin Liu +5
In this study, we present MedS-Bench, a comprehensive benchmark designed to evaluate the performance of large language models (LLMs) in clinical contexts. Unlike existing benchmark…