Papers with Decompose-Then-Verify paradigm
Optimizing Decomposition for Optimal Claim Verification (2025.acl-long)
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| Challenge: | Existing decomposition and verification paradigms ignore their interactions and potential misalignment. |
| Approach: | They propose a reinforcement learning framework that leverages verifier feedback to learn a policy for dynamically decomposing claims to verifier-preferred atomicity. |
| Outcome: | The proposed framework outperforms existing decomposition policies in verification confidence tests . it improves accuracy and confidence by 0.12 on average across varying verifiers, datasets, and atomcities of input claims. |
E-Verify: A Paradigm Shift to Scalable Embedding-based Factuality Verification (2025.findings-emnlp)
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| Challenge: | Existing factuality verification methods follow a Decompose-Then-Verify paradigm, which improves granularity but suffers from poor scalability and efficiency. |
| Approach: | They propose a Decompose-Embed-Interact paradigm that shifts factuality verification from costly text-level reasoning to efficient alignment in embedding space. |
| Outcome: | The proposed paradigm shifts factuality verification from costly text-level reasoning to efficient alignment in embedding space . |
Decomposition Dilemmas: Does Claim Decomposition Boost or Burden Fact-Checking Performance? (2025.naacl-long)
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| Challenge: | Fact-checking pipelines adopt the Decompose-Then-Verify paradigm, where texts are broken down into smaller claims for individual verification and subsequently combined for a veracity decision. |
| Approach: | They propose to categorize decomposition errors and to reveal a trade-off between accuracy gains and noise introduced by decomposing. |
| Outcome: | The proposed analysis provides new insights into understanding current system’s instability and offers guidance for future studies toward improving claim decomposition in fact-checking pipelines. |