Work

mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection

Year: 2026

Type: conference-paper

Abstract: SemEval-2026 Task 9 is focused on multilingual polarization detection.Specifically, it covers the identification of multilingual, multicultural and multievent polarization along three axes (in subtasks), namely detection, type, and manifestation.Online polarization presents a concern, because it is often followed by hate speech, offensive discourse, and social fragmentation.Therefore, its detection before it escalates is crucial for a safer and more inclusive online space.We have coped with this SemEval task by finetuning mid-size LLMs for the sequence-classification task using the QLoRA parameter-efficient finetuning technique.The training data augmented the multilingual (22 languages) training sets by anonymized, lower-cased, upper-cased, and homoglyphied counterparts, making the detection more robust.

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.18653/v1/2026.semeval-1.46

Authors: Dominik Macko, Alok Debnath, Jakub Simko

Institutions: Kempelen Institute of Intelligent Technologies, Trinity College Dublin

Language: English

Topic: Hate Speech and Cyberbullying Detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: European Commission, HORIZON EUROPE Framework Programme, National Supercomputing Center, Korea Institute of Science and Technology Information

Award: Low Resource Artificial Intelligence