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The proposed CCHD framework improves hallucination detection by integrating paraphrase-consistency constraints into the training objective, eliminating the need for expensive data synthesis or inference-time overhead. By leveraging gradient descent-ascent with Lagrange multipliers, this method effectively bounds divergence across semantically equivalent views while maintaining label fidelity. The approach demonstrates superior performance on standard factuality benchmarks using DeBERTa and Flan-T5 backbones, offering a scalable solution for addressing LLM factuality issues.
Constrained Paraphrase Consistency for LLM Hallucination Detection
arXiv:2606.08158v1 Announce Type: new Abstract: Large language models (LLMs) can generate factually inconsistent claims, motivating accurate and scalable hallucination detectors. Prior work largely enlarges training sets via synthesis or new annotations, introducing increasing cost and potential bias while underusing the consistency implied by semantically equivalent paraphrases. We propose Consistency-Constrained Hallucination Detector (CCHD), which formulates training as a constrained optimization problem. The standard cross-entropy on original document-claim pairs is complemented by (i) paraphrase-consistency constraints bounding divergence across paraphrased views, and (ii) label-preservation constraints tying paraphrases to ground truth. We solve the problem by gradient descent-ascent over model parameters and per-view Lagrange multipliers, adding only a few scalar dual variables and no inference-time overhead. With DeBERTa and Flan-T5 backbones, CCHD consistently outperforms strong baselines (FactCG, MiniCheck, and AlignScore) on standard factuality benchmarks, demonstrating its superiority on hallucination detection.
Abstract:Large language models (LLMs) can generate factually inconsistent claims, motivating accurate and scalable hallucination detectors. Prior work largely enlarges training sets via synthesis or new annotations, introducing increasing cost and potential bias while underusing the consistency implied by semantically equivalent paraphrases. We propose Consistency-Constrained Hallucination Detector (CCHD), which formulates training as a constrained optimization problem. The standard cross-entropy on original document-claim pairs is complemented by (i) paraphrase-consistency constraints bounding divergence across paraphrased views, and (ii) label-preservation constraints tying paraphrases to ground truth. We solve the problem by gradient descent-ascent over model parameters and per-view Lagrange multipliers, adding only a few scalar dual variables and no inference-time overhead. With DeBERTa and Flan-T5 backbones, CCHD consistently outperforms strong baselines (FactCG, MiniCheck, and AlignScore) on standard factuality benchmarks, demonstrating its superiority on hallucination detection.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2606.08158 [cs.CL]
(or arXiv:2606.08158v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.08158 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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