[Submitted on 6 Aug 2026]
Title:R2VC: Modular Fact-Checking with Retrieval, Verification, and Confidence Calibration
View a PDF of the paper titled R2VC: Modular Fact-Checking with Retrieval, Verification, and Confidence Calibration, by Dhruv Dixit and 1 other authors
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Abstract:Large language models are increasingly used for automated fact checking, but end-to-end prompting often entangles evidence retrieval, reasoning, and uncertainty estimation, making failures difficult to diagnose and confidence difficult to trust. We present R2VC, a modular retrieve, reason, verify, calibrate architecture for evidence-grounded fact checking with citations and abstention. R2VC combines hybrid sparse+dense retrieval over Wikipedia, a supervised fine-tuned and DPO-aligned generator that produces diverse structured verdict candidates, an external NLI cross-encoder for evidence-based candidate selection, and a lightweight sequence-level calibrator for confidence estimation and selective abstention. On FEVER, an 8B backbone with R2VC achieves 13.74% higher accuracy than baseline. Ablation studies show that verifier-based candidate selection and confidence calibration are the largest contributors to performance. Removing candidate selection drops FEVER accuracy to 76.24%, while removing calibration nearly doubles the Brier score to 0.161. A manual analysis of 250 errors further shows that retrieval failures, especially wrong-entity evidence, remain the dominant bottleneck. Together, these results show that modular fact-checking pipelines can substantially improve both predictive accuracy and confidence reliability in open-domain verification.
Comments: 21 pages, 15 figures
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2609.11955 [cs.CL]
(or arXiv:2609.11955v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.11955
arXiv-issued DOI via DataCite
Submission history
From: Dhruv Dixit [view email] [v1] Thu, 6 Aug 2026 03:35:05 UTC (549 KB)
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