Swushroomsia at SemEval-2025 Task 3: Probing LLMs’ Collective Intelligence for Multilingual Hallucination Detection

AbstractThis paper introduces a system designed for SemEval-2025 Task 3: Mu-SHROOM, which focuses on detecting hallucinations in multilingual outputs generated by large language models (LLMs). Our approach leverages the collective intelligence of multiple LLMs by prompting several models with three distinct prompts to annotate hallucinations. These individual annotations are then merged to create a comprehensive probabilistic annotation. The proposed system demonstrates strong performance, achieving high accuracy in span detection and strong correlation between predicted probabilities and ground truth annotations.