SRCB at SemEval-2025 Task 9: LLM Finetuning Approach based on External Attention Mechanism in The Food Hazard Detection

AbstractThis paper reports on the performance of SRCB’s system in SemEval-2025 Task 9: The Food Hazard Detection Challenge. We develop a system in the form of a pipeline consisting of two parts: 1. Candidate Recall Module, which selects the most probable correct labels from a large number of labels based on BERT model; 2. LLM Prediction Module, which is used to generate the final prediction based on Large Language Models(LLM). Additionally, to address the issue of long prompts caused by an excessive number of labels, we propose a model architecture to reduce resource consumption and improve performance. Our submission achieves the macro-F1 score of 80.39 on Sub-Task 1 and the macro-F1 score of 54.73 on Sub-Task 2. Our system is released at https://github.com/Doraxgui/Document_Attention