Constrained Paraphrase Consistency for LLM Hallucination Detection

Researchers introduce CCHD, a new hallucination detection method for large language models that uses paraphrase consistency constraints to improve factuality checking without expanding training datasets. The approach outperforms existing baselines like FactCG and MiniCheck while adding minimal computational overhead.

Hallucination in LLMs remains a critical challenge as these models proliferate across applications. Current detection methods rely on expanding training datasets through synthesis or annotation, an expensive and potentially biased approach. CCHD addresses this by leveraging semantic equivalence—the insight that paraphrased versions of the same claim should receive consistent factuality assessments from the detector. Rather than collecting more data, the method reformulates the training problem as constrained optimization, where standard cross-entropy loss is augmented with paraphrase-consistency constraints and label-preservation constraints. This ensures the model generalizes better across linguistic variations without explicit retraining on new examples.

The technical approach uses gradient descent-ascent optimization with Lagrange multipliers, elegantly balancing multiple objectives while keeping inference-time costs unchanged. Testing with DeBERTa and Flan-T5 backbones demonstrates consistent improvements over strong baselines across standard benchmarks. This matters because hallucination detection directly impacts reliability in high-stakes applications—legal documents, medical summaries, financial analysis, and scientific claims all depend on accurate factuality assessment. The method's scalability without additional annotation burden addresses a real bottleneck in deploying production systems.

For the AI industry, CCHD exemplifies the shift toward sample-efficient learning and better utilization of inherent data properties. Rather than chasing larger datasets, the approach extracts more value from existing data through principled constraints. This pattern—leveraging consistency properties and mathematical optimization rather than raw scale—could extend across other AI reliability challenges, from robustness to bias detection.

• →CCHD improves LLM hallucination detection using paraphrase consistency constraints without requiring additional training data
• →The method formulates detection as constrained optimization, balancing factuality loss with consistency across semantic variations
• →Implementation adds only scalar dual variables with zero inference-time overhead compared to baseline approaches
• →Testing shows consistent outperformance over FactCG, MiniCheck, and AlignScore on standard factuality benchmarks
• →The approach demonstrates scalable hallucination detection matters for production AI systems in high-stakes domains