title: SemViQA: A Semantic Question Answering System for Vietnamese Information
    Fact-Checking
summary: 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_authors._id: 67c7dbff25d0b3348ddace44
paper_authors.name: Nam V. Nguyen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-05T10:11:25.689Z
paper_authors._id: 64c2bea2ada7df214276913b
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paper_authors.fullname: Nguyen Van Nam
paper_authors.type: user
paper_authors.user: DavidNguyen
paper_authors._id: 67c7dbff25d0b3348ddace45
paper_authors.name: Dien X. Tran
paper_authors.status: claimed_verified
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paper_authors._id: 65217a88fd512a7371866d74
paper_authors.avatarUrl: /avatars/d3ce90d34b1880eb362de58361de1413.svg
paper_authors.fullname: Xuân Diện
paper_authors.type: user
paper_authors.user: xuandin
paper_authors._id: 67c7dbff25d0b3348ddace46
paper_authors.name: Thanh T. Tran
paper_authors._id: 67c7dbff25d0b3348ddace47
paper_authors.name: Anh T. Hoang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-05T10:11:15.231Z
paper_authors._id: 650893f9d48ed98e638a0fd3
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paper_authors.fullname: Hoang Tien Anh
paper_authors.type: user
paper_authors.user: TienAnh
paper_authors._id: 67c7dbff25d0b3348ddace48
paper_authors.name: Tai V. Duong
paper_authors._id: 67c7dbff25d0b3348ddace49
paper_authors.name: Di T. Le
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-05T10:11:20.865Z
paper_authors._id: 6568b9ba3c0881893acc2443
paper_authors.avatarUrl: /avatars/05de2c1e9aaf8ce6f43cf29374df5513.svg
paper_authors.fullname: Le Thanh Di
paper_authors.type: user
paper_authors.user: ThanhDi
paper_authors._id: 67c7dbff25d0b3348ddace4a
paper_authors.name: Phuc-Lu Le
paper_title: SemViQA: A Semantic Question Answering System for Vietnamese Information
    Fact-Checking
paper_summary: 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_ai_keywords: Large Language Models (LLMs) GPT Gemini Semantic-based Evidence Retrieval (SER) Two-step Verdict Classification (TVC) ISE-DSC01 ViWikiFC UIT Data Science Challenge