Domain-Specific Hallucination Detection in Large Language Models
A calibrated DeBERTa detector scored well on HaluEval but transferred poorly to biomedical claims. Builders should tune hallucination checks to their domain instead of trusting a general evaluator.
A DeBERTa-v3 pipeline with MC Dropout and calibration reached **F1 0.915** and AUROC 0.977 on HaluEval; MC Dropout raised accuracy to 93.2%. Removing context cut summarization F1 by 24%.
Treat hallucination detection as domain-specific infrastructure. Keep supporting context available, calibrate uncertainty, and validate the detector on the same claim types your research or coding agent will produce.
A DeBERTa-v3 pipeline with MC Dropout and calibration reached **F1 0.915** and AUROC 0.977 on HaluEval; MC Dropout raised accuracy to 93.2%. Removing context cut summarization F1 by 24%. Treat hallucination detection as domain-specific infrastructure. Keep supporting context available, calibrate uncertainty, and validate the detector on the same claim types your research or coding agent will produce. General-domain training fell to **F1 0.52 on SciFact**; domain-matched PubMedBERT reached F1 0.63. The reported DPO reduction from **85.5% to 37.7%** was measured by the authors’ own detector, so independent validation remains necessary.
This makes hallucination detection a domain- and context-dependent evaluation component rather than a portable generic safeguard. The SciFact drop limits conclusions drawn from strong HaluEval scores, while the DPO result illustrates the circularity risk of using the same detector to certify an intervention. Domain-matched data, retained evidence, calibration, and independent human or task-grounded checks remain necessary.