# LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification

arXiv: https://arxiv.org/abs/2012.13577 · HTML: https://mlsanity.com/paper/2012.13577/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2020.

## Built on by (most cited first, 16 of 16)

- [WiCE: Real-World Entailment for Claims in Wikipedia](https://mlsanity.com/paper/2303.01432/) (2023)
- [Interpretable Multimodal Misinformation Detection with Logic Reasoning](https://mlsanity.com/paper/2305.05964/) (2023)
- [Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning](https://mlsanity.com/paper/2212.01060/) (2022)
- [Optimizing Decomposition for Optimal Claim Verification](https://mlsanity.com/paper/2503.15354/) (2025)
- [Converge to the Truth: Factual Error Correction via Iterative Constrained Editing](https://mlsanity.com/paper/2211.12130/) (2022)
- [QA-NatVer: Question Answering for Natural Logic-based Fact Verification](https://mlsanity.com/paper/2310.14198/) (2023)
- [Zero-Shot Fact Verification via Natural Logic and Large Language Models](https://mlsanity.com/paper/2410.03341/) (2024)
- [Attention with Dependency Parsing Augmentation for Fine-Grained Attribution](https://mlsanity.com/paper/2412.11404/) (2024)
- [Pre-training Multi-party Dialogue Models with Latent Discourse Inference](https://mlsanity.com/paper/2305.15175/) (2023)
- [Harnessing Knowledge and Reasoning for Human-Like Natural Language Generation: A Brief Review](https://mlsanity.com/paper/2212.03747/) (2022)
- [TabVer: Tabular Fact Verification with Natural Logic](https://mlsanity.com/paper/2411.01093/) (2024)
- [Interpretable Multimodal Out-of-context Detection with Soft Logic Regularization](https://mlsanity.com/paper/2406.04756/) (2024)
- [Multi-Evidence based Fact Verification via A Confidential Graph Neural Network](https://mlsanity.com/paper/2405.10481/) (2024)
- [Reasoning Paths as Signals: Augmenting Multi-hop Fact Verification through Structural Reasoning Progression](https://mlsanity.com/paper/2506.07075/) (2025)
- [Althea: The Fact-Checking--Metalearning Tradeoff in AI-Assisted Verification](https://mlsanity.com/paper/2602.11161/) (2025)
- [Validating FKG.in: Soundness Assessment in LLM-Augmented Indian Food Knowledge](https://mlsanity.com/paper/2608.29249/) (2026)

## Abstract (arXiv)

Given a natural language statement, how to verify its veracity against a large-scale textual knowledge source like Wikipedia? Most existing neural models make predictions without giving clues about which part of a false claim goes wrong. In this paper, we propose LOREN, an approach for interpretable fact verification. We decompose the verification of the whole claim at phrase-level, where the veracity of the phrases serves as explanations and can be aggregated into the final verdict according to logical rules. The key insight of LOREN is to represent claim phrase veracity as three-valued latent variables, which are regularized by aggregation logical rules. The final claim verification is based on all latent variables. Thus, LOREN enjoys the additional benefit of interpretability -- it is easy to explain how it reaches certain results with claim phrase veracity. Experiments on a public fact verification benchmark show that LOREN is competitive against previous approaches while enjoying the merit of faithful and accurate interpretability. The resources of LOREN are available at: https://github.com/jiangjiechen/LOREN.