Span-Level Hallucination Detection for LLM-Generated Answers
This work addresses hallucination detection for LLM-generated responses, which is an incremental improvement in a domain-specific task.
The paper tackled the problem of detecting hallucinated spans in LLM-generated answers by integrating Semantic Role Labeling and textual entailment, achieving competitive performance on the Mu-SHROOM dataset.
Detecting spans of hallucination in LLM-generated answers is crucial for improving factual consistency. This paper presents a span-level hallucination detection framework for the SemEval-2025 Shared Task, focusing on English and Arabic texts. Our approach integrates Semantic Role Labeling (SRL) to decompose the answer into atomic roles, which are then compared with a retrieved reference context obtained via question-based LLM prompting. Using a DeBERTa-based textual entailment model, we evaluate each role semantic alignment with the retrieved context. The entailment scores are further refined through token-level confidence measures derived from output logits, and the combined scores are used to detect hallucinated spans. Experiments on the Mu-SHROOM dataset demonstrate competitive performance. Additionally, hallucinated spans have been verified through fact-checking by prompting GPT-4 and LLaMA. Our findings contribute to improving hallucination detection in LLM-generated responses.