Team QUST at S em E val-2025 Task 10: Evaluating Large Language Models in Multiclass Multi-label Classification of News Entity Framing (2025)

Abstract: This paper introduces the participation of the QUST team in subtask 1 of SemEval-2025 Task 10. We evaluate various large language models (LLMs) based on instruction tuning (IT) on subtask 1. Specifically, we first analyze the data statistics, suggesting that the imbalance of label distribution made it difficult for LLMs to be fine-tuned. Subsequently, a voting mechanism is utilized on the predictions of the top-3 models to derive the final submission results. The team participated in all language tracks, achieving 1st place in Hindi (HI), 2nd in Russian (RU), 3rd in Portuguese (PT), 6th in Bulgarian (BG), and 7th in English (EN) on the official test set. We release our system code at: https://github.com/warmth27/SemEval2025_Task10

## introduction semeval-2025 task 10 encourages participants to develop algorithms for multilingual recognition and extraction of narrative tasks from online news @xcite . we participated in subtask 1 (entity framing), which aims to assign fine-grained role labels, covering three main types (protagonists, antagonists, and innocent) to named entities (nes) mentioned in news articles, based on a predefined finegrained role classification system @xcite . this is a multi-label multi-class text-span classification task. during this process, we face several challenges: in subtask 1, we first conduct a quick evaluation by comparing our model with the baseline model. inspired by @xcite and @xcite , we apply instruction tuning (it) to several llms on subtask 1. however, it entails substantial training costs. we first fine-tuning the models in english, then apply it to other languages to minimize finetuning and evaluation time. to enhance the model's performance and generalization ability, we adopt a hard voting based on ensemble learning (jabbar, 2024), in which the top-3 selected models vote to determine the final prediction. experimental results indicate that ensemble learning significantly enhances the model performance. ## conclusion in summary, our team developed an effective approach for subtask 1 of semeval-2025 task 10. we first conduct instruction tuning on large language models on the english dataset and choose the models that perform best, then adapt them to other languages. in the final testing phase, to augment the training data, we incorporate the development data into the training set and select the top-performing model based on evaluation. ultimately, hard voting successfully integrates the advantages of several models, thereby enhancing the prediction accuracy. as the training data for english and bulgarian is relatively limited and we have not yet employed data augmentation methods, future work will explore effective strategies to augment both the quantity and quality of data, thereby enhancing the model's capacity to comprehend texts from diverse languages and cultural backgrounds.