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arxiv: 2606.19819 · v1 · pith:NXNN2IDZnew · submitted 2026-06-18 · 💻 cs.CL · cs.AI

CREDENCE: Claim Reduction for Decomposition & Enhanced Credibility -- Semantic Metrics and Convergence Analysis

Phuong Huu Vu Tran , Thuan Duc Mai , Bach Xuan Le This is my paper

Pith reviewed 2026-06-26 17:55 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords claim decompositionsemantic metricsfact-checkingconvergence analysisatomic claimsrepair pipelinemonotonicity
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The pith

Semantic-F1 using cosine similarity outperforms Jaccard-F1 by 15-32 points and rule repair cuts atomicity violations 47-100 percent.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Decomposing sentences into atomic claims is required for reliable automated fact-checking. Token-overlap metrics like Jaccard-F1 penalise valid paraphrases and prior repair loops lacked termination proofs. Credence introduces Semantic-F1 based on BGE-large cosine similarity to correct the underestimation and supplies formal convergence theorems for the repair pipeline. Rule-based repair is shown to be monotone and finitely terminating under an oracle parser assumption, while LLM self-repair is non-monotone. Experiments on three domain benchmarks with models from 3.8B to closed API confirm the metric gains and violation reductions without fidelity loss.

Core claim

Credence establishes that Semantic-F1, defined via BGE-large cosine similarity fidelity, resolves Jaccard's penalisation of paraphrastic claims for +15-32pp gains, while rule-based repair is monotone and finitely terminating under an oracle parser assumption, reducing Atomicity Violation Rate by 47-100% relative to base models without degrading fidelity, with EPR from 0.94 to 1.00 on easier benchmarks and lower on news-domain cases.

What carries the argument

Semantic-F1 metric via BGE-large cosine similarity together with the rule-based repair pipeline and its monotonicity and termination theorems under oracle parser assumption.

Load-bearing premise

An oracle parser is available that correctly flags atomicity violations so rule-based repair stays monotone and terminates in finite steps.

What would settle it

An experiment showing rule-based repair increases atomicity violations or loops indefinitely when the parser is not an oracle, or Semantic-F1 yielding no downstream fact-checking accuracy gain on a held-out set.

read the original abstract

Decomposing compound sentences into atomic, verifiable claims is a prerequisite for reliable automated fact-checking. Prior work has relied on token-overlap (Jaccard) metrics that systematically underestimate decomposition quality for paraphrastic claims, and has lacked formal termination analysis for the repair loop. We present Credence, a revised claim decomposition and evaluation framework addressing both shortcomings. Our contributions are: (1) Semantic-F1: we use BGE-large cosine similarity fidelity metric that resolves Jaccard's penalisation and improves downstream fact-checking accuracy; (2) Convergence theorems: we formally characterise four properties of the repair pipeline, establishing that rule-based repair is monotone and finitely terminating under an oracle parser assumption; LLM-based self-repair is provably non-monotone and requires an early-exit guard; (3) Three evaluation benchmarks spanning social-media, encyclopaedic, and news domains for cross-domain generalisation measurement; (4) Multi-model benchmarking across four decomposer models (3.8B-12B) and a closed API model. Experiments on SocialClaimSplit, WikiSplitBench, and ClaimDecompBench show that Semantic-F1 outperforms Jaccard-F1 by +15-32pp. EPR ranges from 0.94 to 1.00 on SocialClaimSplit and WikiSplitBench, while ClaimDecompBench includes lower base EPR cases (down to 0.824) due to harder news-domain constructions, and rule-repair reduces the Atomicity Violation Rate (AVR) by 47-100% relative to the base model without degrading fidelity.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces CREDENCE, a claim decomposition framework for fact-checking. It replaces token-overlap Jaccard-F1 with Semantic-F1 (BGE-large cosine similarity) and reports +15-32pp gains on SocialClaimSplit, WikiSplitBench, and ClaimDecompBench. It supplies formal convergence theorems for a repair pipeline, asserting that rule-based repair is monotone and finitely terminating under an oracle parser assumption while LLM self-repair is non-monotone and requires an early-exit guard. Experiments across four decomposer models (3.8B-12B plus closed API) show rule-repair reduces Atomicity Violation Rate by 47-100% relative to base models without fidelity loss; EPR ranges 0.94-1.00 on two benchmarks and down to 0.824 on the news-domain set.

Significance. If the empirical deltas and conditional theorems hold, the work would strengthen automated fact-checking pipelines by replacing a known weakness of overlap metrics with a semantic alternative and by supplying the first formal termination analysis for claim-repair loops. The three-domain benchmark suite and multi-model (open and closed) evaluation are concrete strengths that support cross-domain claims. The explicit formal characterization of four repair properties, even when conditioned, is a positive methodological step that future work can build upon or relax.

major comments (2)
  1. [Abstract, Contribution 2] Abstract, Contribution 2: The claim that rule-based repair is monotone and finitely terminating is explicitly conditioned on an 'oracle parser assumption'. No proof sketch, counterexample analysis, or empirical verification is supplied showing that real parsers (with non-zero error rates) preserve monotonicity or finite termination. Because this assumption is load-bearing for the formal contribution, its unverified status directly limits the applicability of the theorems to the reported experimental domains.
  2. [Abstract] Abstract: The reported performance improvements (Semantic-F1 +15-32pp over Jaccard-F1; AVR reduction 47-100%) are stated without error bars, statistical significance tests, or dataset-level statistics (e.g., claim counts, inter-annotator agreement). These omissions make it impossible to assess whether the observed deltas are robust or could be explained by experimental-setup variance.
minor comments (1)
  1. [Abstract] Ensure the acronym EPR is defined at first use and that all metric definitions (including how cosine similarity is thresholded for Semantic-F1) appear before numerical results are presented.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major point below, indicating where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract, Contribution 2] Abstract, Contribution 2: The claim that rule-based repair is monotone and finitely terminating is explicitly conditioned on an 'oracle parser assumption'. No proof sketch, counterexample analysis, or empirical verification is supplied showing that real parsers (with non-zero error rates) preserve monotonicity or finite termination. Because this assumption is load-bearing for the formal contribution, its unverified status directly limits the applicability of the theorems to the reported experimental domains.

    Authors: The theorems are presented under the explicit oracle parser assumption stated in the manuscript, which enables the formal proofs of monotonicity and finite termination for rule-based repair. We agree that this assumption is idealized and that its relaxation for noisy real parsers is not empirically verified in the current version. To address applicability, we will add a dedicated paragraph in the convergence section that (a) provides a minimal counterexample showing how a single parser error can violate monotonicity, and (b) discusses the empirical robustness observed in our multi-model experiments despite imperfect parsers. This keeps the formal contribution intact while clarifying its scope. revision: partial

  2. Referee: [Abstract] Abstract: The reported performance improvements (Semantic-F1 +15-32pp over Jaccard-F1; AVR reduction 47-100%) are stated without error bars, statistical significance tests, or dataset-level statistics (e.g., claim counts, inter-annotator agreement). These omissions make it impossible to assess whether the observed deltas are robust or could be explained by experimental-setup variance.

    Authors: We agree that the abstract omits these elements. The full manuscript already reports dataset sizes and claim counts in Table 1 and inter-annotator agreement for the human-evaluated subsets. In revision we will (a) augment the abstract with mean ± standard deviation across the four decomposer models, (b) state that all deltas were assessed with paired t-tests (p < 0.01), and (c) add a footnote directing readers to the supplementary material for full per-dataset statistics. These changes will make the reported gains directly verifiable from the abstract. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper defines Semantic-F1 via BGE-large cosine similarity as an independent metric choice that does not reduce to the reported performance deltas. Convergence theorems are presented as formal characterizations explicitly conditioned on the oracle parser assumption, with no equations or quantities shown to be equivalent by construction to fitted inputs or self-referential definitions. No self-citation load-bearing steps, ansatz smuggling, or renaming of known results appear in the derivation chain. The benchmarks and multi-model evaluations supply independent empirical content, rendering the overall derivation self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, invented entities, or additional axioms beyond the oracle parser assumption are stated.

axioms (1)
  • domain assumption Oracle parser assumption for rule-based repair monotonicity and finite termination
    Invoked in contribution (2) to establish termination properties.

pith-pipeline@v0.9.1-grok · 5823 in / 1205 out tokens · 22788 ms · 2026-06-26T17:55:04.004114+00:00 · methodology

discussion (0)

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