VietMed-MCQ: A Consistency-Filtered Data Synthesis Framework for Vietnamese Traditional Medicine Evaluation
arXiv:2601.03792v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable proficiency in general medical domains. However, their performance significantly degrades in specialized, culturally specific domains such as Vietnamese Traditional Medicine (VTM), primarily due to the scarcity of high-quality, structured benchmarks. In this paper, we introduce VietMed-MCQ, a novel multiple-choice question dataset generated via a Retrieval-Augmented Generation (RAG) pipeline with an automated consistency check mechanism. Unlike previous synthetic datasets, our framewor
The proliferation of LLMs highlights the immediate need for specialized, high-quality datasets to overcome cultural and domain-specific performance degradation, especially in non-English or niche fields.
This development addresses a critical barrier to deploying LLMs effectively in non-Western contexts and specialized domains, impacting global AI accessibility and utility beyond mainstream applications.
The creation of consistency-filtered synthetic datasets for culturally specific domains like VTM enables LLMs to perform reliably in areas previously underserved due to data scarcity.
- · Southeast Asian medical practitioners
- · LLM developers
- · Patients in underserved medical domains
- · AI researchers in specialized fields
- · Providers of expensive, manually curated domain-specific datasets
- · Generic LLMs without domain-specific fine-tuning
Improved diagnostic and informational tools for Vietnamese Traditional Medicine become available through LLMs.
Similar data synthesis frameworks are rapidly adopted for other niche cultural or linguistic medical domains globally, accelerating LLM adoption in diverse healthcare systems.
The global standard for LLM evaluation expands to include robustness in culturally specific, low-resource languages and domains, shifting research priorities.
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