Evaluating Large Language Models on Historical Health Crisis Knowledge in Resource-Limited Settings: A Hybrid Multi-Metric Study
arXiv cs.CL / 3/24/2026
💬 OpinionIdeas & Deep AnalysisModels & Research
Key Points
- The paper evaluates several major LLMs (GPT-4, Gemini Pro, Llama 3, and Mistral-7B) on health-crisis question answering for COVID-19, dengue, Nipah, and Chikungunya in Bangladesh’s low-resource setting.
- The authors build a QA dataset sourced from authoritative materials and assess outputs using multiple evaluation methods including semantic similarity, expert-model cross-evaluation, and Natural Language Inference (NLI).
- Results show that LLMs can capture some epidemiological history and health-crisis knowledge, but they also exhibit notable reliability limitations.
- The study concludes that while LLMs have promise for informing policy in resource-constrained environments, their risks must be carefully managed given variable performance.
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