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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.00955 |
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| _version_ | 1866915533170933760 |
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| author | Tran, Dien X. Nguyen, Nam V. Tran, Thanh T. Hoang, Anh T. Duong, Tai V. Le, Di T. Le, Phuc-Lu |
| author_facet | Tran, Dien X. Nguyen, Nam V. Tran, Thanh T. Hoang, Anh T. Duong, Tai V. Le, Di T. Le, Phuc-Lu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00955 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking Tran, Dien X. Nguyen, Nam V. Tran, Thanh T. Hoang, Anh T. Duong, Tai V. Le, Di T. Le, Phuc-Lu Computation and Language Artificial Intelligence 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. |
| title | SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2503.00955 |