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        <title>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</title>
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        <namePart type="given">Manish</namePart>
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            <title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
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            <namePart type="given">Ekaterina</namePart>
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    <abstract>This paper presents the NIT-Agartala-NLPTeam’s submission to SemEval-2026 Task 9on polarization detection in textual data. Thetask 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-votingensemble framework that integrates multiplefine-tuned large language models (LLMs). Theprobabilistic outputs of the individual models are combined using weighted averagingto effectively leverage their complementarystrengths and enhance overall performance.Our system achieved a test macro F1-score of78.6 (26th out of 44 teams) in Subtask 1 and46.0 (18th out of 29 teams) in Subtask 2.</abstract>
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%0 Conference Proceedings
%T 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
%A Kumar, Manish
%A Jamatia, Anupam
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%A Shivam
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F shivam-etal-2026-nit
%X This paper presents the NIT-Agartala-NLPTeam’s submission to SemEval-2026 Task 9on polarization detection in textual data. Thetask 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-votingensemble framework that integrates multiplefine-tuned large language models (LLMs). Theprobabilistic outputs of the individual models are combined using weighted averagingto effectively leverage their complementarystrengths and enhance overall performance.Our system achieved a test macro F1-score of78.6 (26th out of 44 teams) in Subtask 1 and46.0 (18th out of 29 teams) in Subtask 2.
%U https://aclanthology.org/2026.semeval-1.398/
%P 3169-3181

Markdown (Informal)

[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](https://aclanthology.org/2026.semeval-1.398/) (Shivam et al., SemEval 2026)

• 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 (Shivam et al., SemEval 2026)

ACL

• Shivam, Manish Kumar, and Anupam Jamatia. 2026. 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. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 3169–3181, San Diego, California, USA. Association for Computational Linguistics.