# DecMetrics: Structured Claim Decomposition Scoring for Factually Consistent LLM Outputs

arXiv: https://arxiv.org/abs/2509.04483 · HTML: https://mlsanity.com/paper/2509.04483/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 31 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024)
- [FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios](https://mlsanity.com/paper/2307.13528/) (2023)
- [Long-form factuality in large language models](https://mlsanity.com/paper/2403.18802/) (2024)
- [Decomposition Dilemmas: Does Claim Decomposition Boost or Burden Fact-Checking Performance?](https://mlsanity.com/paper/2411.02400/) (2024)
- [Loki: An Open-Source Tool for Fact Verification](https://mlsanity.com/paper/2410.01794/) (2024)

## Built on by (most cited first, 4 of 4)

- [The Last Human-Written Paper: Agent-Native Research Artifacts](https://mlsanity.com/paper/2604.24658/) (2026)
- [Atomic-SNLI: Fine-Grained Natural Language Inference through Atomic Fact Decomposition](https://mlsanity.com/paper/2601.06528/) (2026)
- [CREDENCE: Claim Reduction for Decomposition & Enhanced Credibility -- Semantic Metrics and Convergence Analysis](https://mlsanity.com/paper/2606.19819/) (2026)
- [Confidence Reasoning Graphs: Structured Confidence Estimation for LLM Agents](https://mlsanity.com/paper/2610.07948/) (2026)

## Abstract (arXiv)

Claim decomposition plays a crucial role in the fact-checking process by breaking down complex claims into simpler atomic components and identifying their unfactual elements. Despite its importance, current research primarily focuses on generative methods for decomposition, with insufficient emphasis on evaluating the quality of these decomposed atomic claims. To bridge this gap, we introduce DecMetrics, which comprises three new metrics: textttCOMPLETENESS, textttCORRECTNESS, and textttSEMANTIC ENTROPY, designed to automatically assess the quality of claims produced by decomposition models. Utilizing these metrics, we develop a lightweight claim decomposition model, optimizing its performance through the integration of these metrics as a reward function. Through automatic evaluation, our approach aims to set a benchmark for claim decomposition, enhancing both the reliability and effectiveness of fact-checking systems.