Authors: Ipek Baris Schlicht, Zhixue Zhao, Burcu Sayin, Lucie Flek, Paolo Rosso
Abstract: Equitable access to reliable health information is vital for public health,
but the quality of online health resources varies by language, raising concerns
about inconsistencies in Large Language Models (LLMs) for healthcare. In this
study, we examine the consistency of responses provided by LLMs to
health-related questions across English, German, Turkish, and Chinese. We
largely expand the HealthFC dataset by categorizing health-related questions by
disease type and broadening its multilingual scope with Turkish and Chinese
translations. We reveal significant inconsistencies in responses that could
spread healthcare misinformation. Our main contributions are 1) a multilingual
health-related inquiry dataset with meta-information on disease categories, and
2) a novel prompt-based evaluation workflow that enables sub-dimensional
comparisons between two languages through parsing. Our findings highlight key
challenges in deploying LLM-based tools in multilingual contexts and emphasize
the need for improved cross-lingual alignment to ensure accurate and equitable
healthcare information.
Source: http://arxiv.org/abs/2501.14719v1
