Howard University - AI4PC at SemEval-2025 Task 3: Logit-based Supervised Token Classification for Multilingual Hallucination Span Identification Using XGBOD

AbstractThis paper describes our system for SemEval-2025 Task 3, Mu-SHROOM, which focuses on detecting hallucination spans in multilingual LLM outputs. We reframe hallucination detection as a point-wise anomaly detection problem by treating logits as time-series data. Our approach extracts features from token-level logits, addresses class imbalance with SMOTE, and trains an XGBOD model for probabilistic character-level predictions. Our system, which relies solely on information derived from the logits and token offsets (using pretrained tokenizers), achieves competitive intersection-over-union (IoU) and correlation scores on the validation and test set.