Bilingual Performance of Large Language Models in Answering Consumer Health Questions in English and Chinese: Comparative Benchmark Study.

backgroundLarge language models (LLMs) are increasingly used as health information intermediaries. Whether they provide comparable accuracy and communication quality across languages has direct implications for health information equity; however, systematic bilingual evaluations remain limited.

objectiveThis study aimed to provide a preliminary bilingual benchmark evaluating whether 11 LLMs deliver comparable accuracy and communication quality when answering identical consumer health questions in English and Chinese.

methodsWe conducted a controlled evaluation of 11 LLMs (GPT-4.5, Claude Sonnet 4, Gemini 2.5 Flash, Grok 3, DeepSeek R1, Qwen 3, Doubao, Kimi k1.5, Hunyuan T1, ERNIE X1 Turbo, and ChatGLM 4) using 150 binary consumer health questions from the Text Retrieval Conference Health Misinformation Track (2019, 2021, and 2022). All models were accessed through official public-facing web interfaces during May 2025. Models were assessed under 2 full-benchmark prompting conditions (no-context and expert), evaluating accuracy, comprehensiveness, precision, and understandability. Four post hoc error-correction strategies (chain-of-thought [CoT], retrieval-augmented generation [RAG], CoT+RAG, and error attribution) were applied to baseline-incorrect responses. Composite ranking used the technique for order of preference by similarity to ideal solution (TOPSIS), with sensitivity analysis across 3 weighting schemes. Generalized estimating equations and linear mixed models with Benjamini-Hochberg false discovery rate (FDR) correction were applied using a full 3-way interaction specification (model×language×prompt).

resultsEnglish and Chinese inputs showed comparable overall accuracy under no-context conditions (1572/1650, 95.27% vs 1548/1650, 93.82%), with no significant language main effect (β=0.00; P=.99). No language main effects for any individual model remained significant after FDR correction. TOPSIS analysis identified ChatGPT and Qwen as the most consistently top-ranked models (tier 1 in 12/12 condition×weight-scheme combinations). A model-specific language interaction emerged for communication quality: DeepSeek showed a significant English-language decrement in understandability (β=-0.73; FDR=-0.016), while its decrements in precision and comprehensiveness were not significant after correction. One 3-way interaction survived: Grok showed a disproportionate accuracy reduction when English input and expert prompting were combined (β=-1.88; FDR=-0.022). Among post hoc correction strategies, error attribution achieved the highest correction rate (Δ55.56%), although this condition provided models with privileged information.

conclusionsContemporary LLMs achieved high binary accuracy on consumer health questions in both English and Chinese, with no significant aggregate language effect. The only robust model-specific language interaction was DeepSeek's English understandability decrement, independently confirmed by TOPSIS tier analysis. These findings suggested that cross-linguistic communication quality concerns were model-specific rather than universal and warrant targeted monitoring.