SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking (2026.acl-industry)
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| Challenge: | Existing methods struggle with semantic ambiguity, homonyms, and complex linguistic structures, often trading accuracy for efficiency. |
| Approach: | They propose a Vietnamese fact-checking framework that integrates SER and TVC to achieve 78.97% strict accuracy. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy with 78.97% strict accuracy on ISE-DSC01 and 80.82% on ViWikiFC while maintaining competitive accuracy. |
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| Challenge: | Existing Vietnamese Question Answering (QA) datasets do not explore the model’s ability to perform advanced reasoning and provide evidence to explain the answer. |
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| Challenge: | Existing studies on Large Language Models (LLMs) are limited to single domains or curated datasets. |
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Sarfraz Ahmad, Hasan Iqbal, Momina Ahsan, Numaan Naeem, Muhammad Ahsan Riaz Khan, Arham Riaz, Muhammad Arslan Manzoor, Yuxia Wang, Preslav Nakov
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| Challenge: | Large language models (LLMs) struggle with factual accuracy in knowledge-intensive domains like healthcare. |
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| Challenge: | Existing abstractive question-answering datasets in Vietnamese are lacking . |
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FaVIQ: FAct Verification from Information-seeking Questions (2022.acl-long)
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| Challenge: | Existing fact-checking approaches focus on claims made in English due to data scarcity issue in other languages. |
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TruthTrap: A Bilingual Benchmark for Evaluating Factually Correct Yet Misleading Information in Question Answering (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) are increasingly used to answer factual, information-seeking questions (ISQs). |
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