Diversity-Oriented Fine-Tuning Improves Hallucination Detection in LLMs
A new preprint proposes diversity-oriented fine-tuning strategies—using Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO)—to improve semantic-entropy-based hallucination detection in large language models. By encouraging more varied generations, these methods reduce cases where models repeatedly produce identical incorrect answers, which often evade detection. Experiments show that the approach improves detection effectiveness, matching or surpassing current state-of-the-art methods.
Why it matters: This work introduces a practical fine-tuning approach that directly enhances the reliability of hallucination detection in large language models, addressing a key challenge in their deployment.
Full story at: arXiv AI/ML ↗