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Main Authors: Guo, Chenlu, Xu, Nuo, Chang, Yi, Wu, Yuan
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2409.15766
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author Guo, Chenlu
Xu, Nuo
Chang, Yi
Wu, Yuan
author_facet Guo, Chenlu
Xu, Nuo
Chang, Yi
Wu, Yuan
contents With the rapid development of large language models (LLMs), assessing their performance on health-related inquiries has become increasingly essential. The use of these models in real-world contexts-where misinformation can lead to serious consequences for individuals seeking medical advice and support-necessitates a rigorous focus on safety and trustworthiness. In this work, we introduce CHBench, the first comprehensive safety-oriented Chinese health-related benchmark designed to evaluate LLMs' capabilities in understanding and addressing physical and mental health issues with a safety perspective across diverse scenarios. CHBench comprises 6,493 entries on mental health and 2,999 entries on physical health, spanning a wide range of topics. Our extensive evaluations of four popular Chinese LLMs highlight significant gaps in their capacity to deliver safe and accurate health information, underscoring the urgent need for further advancements in this critical domain. The code is available at https://github.com/TracyGuo2001/CHBench.
format Preprint
id arxiv_https___arxiv_org_abs_2409_15766
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle CHBench: A Chinese Dataset for Evaluating Health in Large Language Models
Guo, Chenlu
Xu, Nuo
Chang, Yi
Wu, Yuan
Computation and Language
With the rapid development of large language models (LLMs), assessing their performance on health-related inquiries has become increasingly essential. The use of these models in real-world contexts-where misinformation can lead to serious consequences for individuals seeking medical advice and support-necessitates a rigorous focus on safety and trustworthiness. In this work, we introduce CHBench, the first comprehensive safety-oriented Chinese health-related benchmark designed to evaluate LLMs' capabilities in understanding and addressing physical and mental health issues with a safety perspective across diverse scenarios. CHBench comprises 6,493 entries on mental health and 2,999 entries on physical health, spanning a wide range of topics. Our extensive evaluations of four popular Chinese LLMs highlight significant gaps in their capacity to deliver safe and accurate health information, underscoring the urgent need for further advancements in this critical domain. The code is available at https://github.com/TracyGuo2001/CHBench.
title CHBench: A Chinese Dataset for Evaluating Health in Large Language Models
topic Computation and Language
url https://arxiv.org/abs/2409.15766