mlsanityData to 14 Oct 2026

BiDeV: Bilateral Defusing Verification for Complex Claim Fact-Checking

arXiv 2502.16181 ↗First posted 22 Feb 2025cs.CL cs.AIcited by 8

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Abstract (arXiv)

Complex claim fact-checking performs a crucial role in disinformation detection. However, existing fact-checking methods struggle with claim vagueness, specifically in effectively handling latent information and complex relations within claims. Moreover, evidence redundancy, where nonessential information complicates the verification process, remains a significant issue. To tackle these limitations, we propose Bilateral Defusing Verification (BiDeV), a novel fact-checking working-flow framework integrating multiple role-played LLMs to mimic the human-expert fact-checking process. BiDeV consists of two main modules: Vagueness Defusing identifies latent information and resolves complex relations to simplify the claim, and Redundancy Defusing eliminates redundant content to enhance the evidence quality. Extensive experimental results on two widely used challenging fact-checking benchmarks (Hover and Feverous-s) demonstrate that our BiDeV can achieve the best performance under both gold and open settings. This highlights the effectiveness of BiDeV in handling complex claims and ensuring precise fact-checking

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 20 of its 36 references that are in the map. First posted on arXiv 22 Feb 2025.

Builds on

20 of 36 references.

References · 20

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  5. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  6. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  7. 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,254
  8. 2021A Survey on Automated Fact-Checkingcited by 378
  9. 2020HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verificationcited by 201
  10. 2023The science of fake newscited by 97
  11. 2018Automated Fact Checking: Task formulations, methods and future directionscited by 96
  12. 2023Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkerscited by 87
8 more
  1. 2019BERT for Evidence Retrieval and Claim Verificationcited by 57
  2. 2024From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake Newscited by 51
  3. 2023DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacycited by 34
  4. 2023FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answeringcited by 24
  5. 2024The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agentscited by 24
  6. 2021Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detectioncited by 17
  7. 2021A Multi-Level Attention Model for Evidence-Based Fact Checkingcited by 13
  8. 2024MockLLM: A Multi-Agent Behavior Collaboration Framework for Online Job Seeking and Recruitingcited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 2.

8 papers.