SemViQA: A Semantic Question Answering System for Vietnamese Information
  Fact-Checking
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.
67c7dbff25d0b3348ddace44
Nam V. Nguyen
claimed_verified
2025-03-05T10:11:25.689Z
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Nguyen Van Nam
user
DavidNguyen
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Dien X. Tran
claimed_verified
2025-03-05T10:11:17.563Z
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Xuân Diện
user
xuandin
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Thanh T. Tran
67c7dbff25d0b3348ddace47
Anh T. Hoang
claimed_verified
2025-03-05T10:11:15.231Z
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Hoang Tien Anh
user
TienAnh
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Tai V. Duong
67c7dbff25d0b3348ddace49
Di T. Le
claimed_verified
2025-03-05T10:11:20.865Z
6568b9ba3c0881893acc2443
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Le Thanh Di
user
ThanhDi
67c7dbff25d0b3348ddace4a
Phuc-Lu Le
SemViQA: A Semantic Question Answering System for Vietnamese Information
  Fact-Checking
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.
SemViQA combines Semantic-based Evidence Retrieval and Two-step Verdict Classification to enhance Vietnamese fact-checking, achieving top performance with improved speed.
Large Language Models LLMS Semantic-based Evidence Retrieval SER Two-step Verdict Classification TVC ISE-DSC01 ViWikiFC UIT Data Science Challenge