# Do LLMs Provide Consistent Answers to Health-Related Questions across Languages?

arXiv: https://arxiv.org/abs/2501.14719 · HTML: https://mlsanity.com/paper/2501.14719/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2025.

## Built on by (most cited first, 9 of 9)

- [On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.00597/) (2025)
- [It's All About In-Context Learning! Teaching Extremely Low-Resource Languages to LLMs](https://mlsanity.com/paper/2508.19089/) (2025)
- [Found in Translation: Measuring Multilingual LLM Consistency as Simple as Translate then Evaluate](https://mlsanity.com/paper/2505.21999/) (2025)
- [MIRA: A Bilingual Benchmark for Medical Information Response Audit](https://mlsanity.com/paper/2605.28025/) (2026)
- [Zoom In Disparities in Healthcare LLM Q&A](https://mlsanity.com/paper/2510.17476/) (2025)
- [Towards Personalized Explanations for Health Simulations: A Mixed-Methods Framework for Stakeholder-Centric Summarization](https://mlsanity.com/paper/2509.04646/) (2025)
- [Evaluating Health Misinformation in Low-Resource Languages: Integrating Small Language Models with a Culturally-Sensitive Responsible NLP Framework (Bangla as a Case Study)](https://mlsanity.com/paper/2607.12336/) (2026)
- [Perspectives on Cross-Lingual Consistency in LLMs for Medical Questions](https://mlsanity.com/paper/2609.07687/) (2026)
- [HerHealthEval: Evaluating Multilingual and Register-Sensitive Understanding of Women's Health Communication](https://mlsanity.com/paper/2609.20684/) (2026)

## Abstract (arXiv)

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.