NIT-Agartala-NLP-Team at SemEval-2026 Task 9: A Weighted Soft-Voting Ensemble Framework of Fine-Tuned LLMs for Binary and Multi-Label Polarization Detection
Year: 2026
Type: conference-paper
Abstract: This paper presents the NIT-Agartala-NLP-Team's submission to SemEval-2026 Task 9 on polarization detection in textual data.The task comprises two subtasks: (i) binary classification to distinguish polarized from nonpolarized content, and (ii) multi-label classification to identify the specific type(s) of polarization.We propose a weighted soft-voting ensemble framework that integrates multiple fine-tuned large language models (LLMs).The probabilistic outputs of the individual models are combined using weighted averaging to effectively leverage their complementary strengths and enhance overall performance.Our system achieved a test macro F 1 -score of 78.6 (26 th out of 44 teams) in Subtask 1 and 46.0 (18 th out of 29 teams) in Subtask 2.
Authors: Shivam, Manish Kumar, Anupam Jamatia
Institution: National Institute of Technology Agartala
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166869853