title: Optimizing Decomposition for Optimal Claim Verification
summary: Current research on the Decompose-Then-Verify paradigm for
  evaluating the factuality of long-form text typically treats decomposition and
  verification in isolation, overlooking their interactions and potential
  misalignment. We find that existing decomposition policies, typically
  hand-crafted demonstrations, do not align well with downstream verifiers in
  terms of atomicity -- a novel metric quantifying information density -- leading
  to suboptimal verification results. We formulate finding the optimal
  decomposition policy for optimal verification as a bilevel optimization
  problem. To approximate a solution for this strongly NP-hard problem, we
  propose dynamic decomposition, a reinforcement learning framework that
  leverages verifier feedback to learn a policy for dynamically decomposing
  claims to verifier-preferred atomicity. Experimental results show that dynamic
  decomposition outperforms existing decomposition policies, improving
  verification confidence by 0.07 and accuracy by 0.12 (on a 0-1 scale) on
  average across varying verifiers, datasets, and atomcities of input claims.
paper_authors._id: 67dc1db899d011efd7da645e
paper_authors.name: Yining Lu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-20T19:17:03.538Z
paper_authors._id: 642f742270daaa6e7209a2c8
paper_authors.avatarUrl: /avatars/746fb840c220329f69a17905ea519322.svg
paper_authors.fullname: Yining Lu
paper_authors.type: user
paper_authors.user: ylu610
paper_authors._id: 67dc1db899d011efd7da645f
paper_authors.name: Noah Ziems
paper_authors._id: 67dc1db899d011efd7da6460
paper_authors.name: Hy Dang
paper_authors._id: 67dc1db899d011efd7da6461
paper_authors.name: Meng Jiang
paper_title: Optimizing Decomposition for Optimal Claim Verification
paper_summary: Current research on the Decompose-Then-Verify paradigm for
  evaluating the factuality of long-form text typically treats decomposition and
  verification in isolation, overlooking their interactions and potential
  misalignment. We find that existing decomposition policies, typically
  hand-crafted demonstrations, do not align well with downstream verifiers in
  terms of atomicity -- a novel metric quantifying information density -- leading
  to suboptimal verification results. We formulate finding the optimal
  decomposition policy for optimal verification as a bilevel optimization
  problem. To approximate a solution for this strongly NP-hard problem, we
  propose dynamic decomposition, a reinforcement learning framework that
  leverages verifier feedback to learn a policy for dynamically decomposing
  claims to verifier-preferred atomicity. Experimental results show that dynamic
  decomposition outperforms existing decomposition policies, improving
  verification confidence by 0.07 and accuracy by 0.12 (on a 0-1 scale) on
  average across varying verifiers, datasets, and atomcities of input claims.
paper_ai_summary: Dynamic decomposition, a reinforcement learning approach, optimizes claim decomposition for better verification confidence and accuracy by adjusting to verifier preferences.
paper_ai_keywords: decomposition verification atomicity bilevel optimization reinforcement learning dynamic decomposition