SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking

Nguyen Van Nam, Xuân Diện, Thanh T. Tran, Hoang Tien Anh, Tai V. Duong, Le Thanh Di, Phuc-Lu Le

SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking: 28 upvotes on Hugging Face Daily Papers, #1 of 26 papers on 2025-03-05. Day-by-day upvote history.

The rise of misinformation, exacerbated by Large Language Models (LLMs) like GPT and Gemini, demands robust fact-checking solutions, especially for low-resource languages like Vietnamese. Existing methods struggle with semantic ambiguity, homonyms, and complex linguistic structures, often trading accuracy for efficiency. We introduce SemViQA, a novel Vietnamese fact-checking framework integrating Semantic-based Evidence Retrieval (SER) and Two-step Verdict Classification (TVC). Our approach balances precision and speed, achieving state-of-the-art results with 78.97\% strict accuracy on ISE-DSC01 and 80.82\% on ViWikiFC, securing 1st place in the UIT Data Science Challenge. Additionally, SemViQA Faster improves inference speed 7x while maintaining competitive accuracy. SemViQA sets a new benchmark for Vietnamese fact verification, advancing the fight against misinformation. The source code is available at: https://github.com/DAVID-NGUYEN-S16/SemViQA.

Paper page on Hugging Face · arXiv

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