Domain-Specific Hallucination Detection in Large Language Models
Detecting factual hallucinations remains notoriously difficult once models venture outside general conversation. This paper presents a multi-signal detection pipeline pairing fine-tuned DeBERTa-v3 with Monte Carlo Dropout uncertainty estimation and temperature calibration, reaching an F1 of 0.915 on the HaluEval benchmark. The authors also use this verification signal to guide Direct Preference Optimization (DPO), cutting hallucination rates in Qwen2.5-0.5B from 85.5% to 37.7%. However, testing on biomedical data reveals a steep performance drop for generalist detectors (F1=0.52), demonstrating that fact verification in specialized settings still demands domain-matched models.
Why it's worth reading
It provides a practical pipeline for hallucination verification while quantitatively exposing the severe domain-transfer gap, demonstrating how detection signals can directly supervise DPO alignment in smaller models.