Domain-Specific Hallucination Detection in Large Language Models

The multi-signal detection pipeline combining fine-tuned DeBERTa-v3, Monte Carlo (MC) Dropout, and temperature-scaled calibration achieves an F1 score of 0.915 and AUROC of 0.977 on the HaluEval benchmark.

Key Performance Details: Accuracy Improvement: MC Dropout inference boosts accuracy from 91.3% to 93.2% by averaging 20 stochastic forward passes. Per-Task Performance: The pipeline achieves high F1 scores in Question Answering (0.97) and Summarization (0.96), though Dialogue tasks are lower (0.82). Context Dependency: Ablation studies confirm the model relies on genuine entailment reasoning; removing knowledge context causes summarization F1 to drop by 24%. Mitigation: Applying Direct Preference Optimization (DPO) to a generator model reduced its hallucination rate by 55.9% (from 85.5% to 37.7%). * Domain Transfer: General-domain training transfers poorly to biomedical tasks (F1=0.52), demonstrating that domain-matched pre-training (e.g., PubMedBERT) is necessary for specialized detection.

The material addresses hallucination detection in large language models, with a focus on domain-specific settings where factual errors can be subtle, terminology-heavy, and context-dependent. It presents a multi-signal detection pipeline built around DeBERTa-v3, MC Dropout, and temperature calibration. Rather than treating hallucination detection as a single binary classification problem, the approach combines learned semantic representations with uncertainty-aware confidence estimates, allowing the system to distinguish likely factual claims from potentially hallucinated ones using multiple complementary cues.

A key contribution is the integration of three related signals: DeBERTa-v3 captures fine-grained linguistic and contextual patterns associated with hallucination, MC Dropout provides a stochastic uncertainty estimate by simulating model variability through dropout at inference time, and temperature calibration aligns the model’s predicted confidence with empirical correctness. On HaluEval, the pipeline reports an F1 score of 0.915 and an AUROC of 0.977, indicating strong discriminative performance for identifying hallucinated statements.

This work matters because reliable hallucination detection is essential for deploying LLMs in high-stakes domains such as medicine, law, finance, and technical support. A calibrated, uncertainty-aware detector can be used not only to flag likely false claims but also to trigger downstream safeguards such as retrieval augmentation, human review, or abstention when confidence is low. More broadly, the results suggest that combining representation learning with uncertainty estimation and probability calibration is a practical direction for building more trustworthy factual-reliability monitoring for LLM systems.