From Snippets to Semantics: Rethinking Evidence Granularity for Multilingual Fact Verification
arXiv:2605.26755v1 Announce Type: new Abstract: Multilingual fact verification requires evidence that is both relevant and sufficiently complete for reliable factuality prediction. However, existing systems often rely on search snippets, sentence-level evidence, or locally segmented passages, which can miss decisive context and produce fragmented evidence. To overcome these limitations, we propose SEEK, a Semantic Evidence Extraction with an adaptive chunKing framework that constructs coherent evidence chunks from full fact-checking articles by identifying semantic topic transitions and preser
The proliferation of misinformation and deepfakes across languages necessitates more robust and accurate fact-verification methods, especially as AI models become more sophisticated in content generation.
Improved multilingual fact verification directly impacts the reliability of information, which is critical for decision-making in various sectors and for combating disinformation campaigns.
Current fact-checking systems often struggle with fragmented or insufficient evidence; SEEK proposes a method to construct more coherent and semantically rich evidence chunks, potentially leading to more accurate and reliable multilingual fact-checking.
- · Fact-checking organizations
- · AI ethics and safety researchers
- · Multilingual information consumers
- · Social media platforms
- · Producers of misinformation
- · Systems relying on fragmented evidence
- · AI models without robust fact-checking integration
Increased accuracy and efficiency of multilingual fact-checking processes.
Reduced spread and impact of multilingual disinformation, fostering more informed global discourse.
Potential for integration into large language models, enhancing their veracity during content generation and retrieval.
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Read at arXiv cs.CL