Domain-Specific Hallucination Detection in Large Language Models

Varun Teja Chundru, Debasmita Biswas|September 10, 2026arXiv

Key Takeaway

Hallucination detectors trained on general text fail dramatically on specialized domains—use domain-matched models like PubMedBERT for biomedical content, and combine classification with uncertainty quantification for best results.

Summary

This paper builds a hallucination detector for LLMs using a three-part system: a fine-tuned DeBERTa classifier, uncertainty estimation via MC Dropout, and temperature calibration. Tested on HaluEval, it achieves 91.5% F1 on general tasks but struggles cross-domain (52% F1 on biomedical text), showing that domain-specific pre-training is essential for reliable hallucination detection.

evaluation safety training

Key Terms

hallucination monte-carlo-dropout calibration direct-preference-optimization domain-specific-fine-tuning