DUTIR at S em E val-2025 Task 10: A Large Language Model-based Approach for Entity Framing in Online News (2025)

Abstract: We propose a multilingual text processing framework that combines multilingual translation with data augmentation, QLoRA-based multi-model fine-tuning, and GLM-4-Plus-based ensemble classification. By using GLM-4-Plus to translate multilingual texts into English, we enhance data diversity and quantity. Data augmentation effectively improves the model’s performance on imbalanced datasets. QLoRA fine-tuning optimizes the model and reduces classification loss. GLM-4-Plus, as a meta-classifier, further enhances system performance. Our system achieved first place in three languages (English, Portuguese and Russian).

## introduction with the increasing prevalence of the internet, people can easily access diverse information, which has also facilitated the propagation of misinformation more readily compared to traditional media. public perceptions of events are often influenced by these harmful false narratives and propaganda, particularly regarding major crisis incidents. consequently, misinformation identification has become crucial, prompting growing research @xcite to analyze and categorize entities in textual information. the semeval-2025 task 10 @xcite focuses on multilingual representation and narrative extraction from online news, aiming to advance research and development of novel analytical capabilities to support end-users in analyzing news ecosystems and identifying characteristics of manipulation attempts. the task organizers construct a dataset @xcite comprising 1,378 news articles focusing on the ukraine-russia war and climate change, with role annotations applied to over 5,800 entities. this task comprises three subtasks and we participate in subtask 1 (entity framing). specifically, given a news article and a list of named entity (ne) mentions within it, the objective is to assign one or multiple roles to each mention using a predefined refined role taxonomy. this taxonomy encompasses three primary role types: protagonists, antagonists and innocent, forming a multi-label multi-class text span classification task. entity framing subtask presents two main challenges. first, as a multilingual task involving five languages (bulgarian, english, hindi, european portuguese and russian), traditional methods exhibit limited modeling capabilities for large-scale, complex multilingual tasks, particularly in handling long sequences and intricate semantics. the emergence of llms @xcite addresses this challenge effectively through their robust generalization capabilities and multilingual data integration. second, this subtask requires multi-label multi-class classification where each entity must be assigned to one of three primary roles, with further granularity in secondary subcategories. for instance, the protagonist category includes finer-grained roles such as guardian, martyr, peacemaker, rebel, underdog and virtuous. this hierarchy demands enhanced semantic comprehension and classification capabilities from models. to address these challenges, we implemented an llm-based pipeline. initially, we translated all non-english data into english for unified processing and constructed a task-specific dataset. we then fine-tuned multiple foundational large language models @xcite @xcite on this dataset. subsequently, we employed a state-of-the-art llm by designed ensemble prompts to aggregate decisions from multiple llms, generating final classification results. our proposed method demonstrated strong performance by achieving first place in three out of the five languages evaluated in subtask 1. ## background named entity recognition (ner) has long been a key research direction in the field of natural language processing (nlp) @xcite . ner refers to the task of identifying entities with specific meanings in text, such as person names, locations and others, and annotating them accordingly. essentially, it is a sequence labeling task aimed at classifying each word or phrase in a text as belonging to a specific named entity category or not belonging to any named entity category. in recent years, with the emergence of an increasing number of open-source large models @xcite and the introduction of various fine-tuning techniques @xcite , llms have achieved significant progress in ner tasks @xcite . in this study @xcite , they collect and use 14 ner datasets covering english, french, and spanish, and compare the performance of generative llms with few-shot prompts and traditional masked-based models in both general and clinical domains. gpt-ner @xcite cleverly transforms the traditional ner sequence labeling task into a generation task that is easier for llms to handle, using special tokens to mark the entities to be extracted. additionally, it constructs fewshot prompt words by retrieving semantically similar examples from the input via knn, effectively bridging the gap between the ner task and llms. ## system overview as shown in figure 1 , the overall structure of our system includes the following key components: instruction-tuning dataset construction based on multilingual text translation and data augmentation, multi-model fine-tuning based on qlora, and ensemble classification based on glm-4-plus. ## experimental setup the dataset originates from subtask 1 of task 10 in semeval 2025, comprising news articles in plain text format across five languages: english (en), portuguese (pt), russian (ru), bulgarian (bg) and hindi (hi). during the experimental phase, we generated individual data records for each entity mention, with the statistical summary presented in to address these challenges, we employed strategies of data translation and augmentation. specifically, we leveraged llms to translate all articles from different languages into english, resulting in 5,617 data records. building on this, we conducted data augmentation operations such as synonym replacement for underrepresented categories, adding additional 700 data records. ultimately, all datasets were merged to form a complete dataset containing 6,317 training data records. for task evaluation, the official assessment utilized multiple metrics to comprehensively measure model performance, including exact match ratio, micro precision (micro p), micro recall (micro r), micro f1 score (micro f1), and accuracy for the main role. among these, the exact match ratio was the primary evaluation metric. during the training process, a batch size of 4 was used, the learning rate was set to 1e-4, and a maximum truncation length of 4096 was set to accommodate text inputs of varying lengths. additionally, the adamw optimizer was selected to further enhance the model's training efficiency and generalization ability. all experiments were conducted on a single nvidia l40 gpu. ## conclusion this paper presents the system we designed for subtask 1 of semeval-2025 task 10. we propose a multilingual text processing framework that combines multilingual translation with data augmentation, qlora-based multi-model fine-tuning, and glm-4-plus-based ensemble classification. by using glm-4-plus to translate multilingual texts into english, we enhance data diversity and quantity. data augmentation effectively improves the model's performance on imbalanced datasets. qlora fine-tuning optimizes the model and reduces classification loss. glm-4-plus, as a metaclassifier, further enhances system performance. our system achieved first place in three languages (english, portuguese and russian). in the future, we will focus on improving long-text processing and optimizing llms fine-tuning techniques.