OPI-DRO-HEL at SemEval-2025 Task 9: Integrating Transformer-Based Classification with LLM-Assisted Few-Shot Learning for Food Hazard Detection

AbstractIn this paper, we propose a hybrid approach for food hazard detection that combines a fine-tuned RoBERTa classifier with few-shot learning using an LLM model (GPT-3.5-turbo). We address challenges related to unstructured text and class imbalance by applying class weighting and keyword extraction (KeyBERT, YAKE, and Sentence-BERT). When RoBERTa’s confidence falls below a given threshold, a structured prompt which comprising the title, extracted keywords, and a few representative examples is used to re-evaluate the prediction with ChatGPT.