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        <title>AI4PC-Howard University at SemEval-2026 Task 9: Evaluating Teacher-Student Weak Supervision and Direct LLM Prompting for Multilingual Political Polarization Detection</title>
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            <namePart type="given">Ekaterina</namePart>
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    <abstract>We describe the AI4PC–Howard University submission to SemEval-2026 Task 9, Subtask 1 on multilingual political polarization detection across 22 languages. We investigated two approaches: (1) a weakly supervised teacher–student framework in which a large language model (LLM) generated pseudo-labels to train an XLM-RoBERTa-base classifier, and (2) (2) a context-engineered prompt-based approach using Meta-Llama-3.1-8B-Instruct. The teacher–student approach exhibited instability under distribution shift and collapsed toward majority predictions at test time. Consequently, our final submission used direct inference with Meta-Llama-3.1-8B-Instruct. While this approach produced competitive macro-F1 across evaluated languages, results reveal strong positive-class bias and substantial precision–recall imbalance. Our findings highlight limitations of weak supervision for subjective political tasks and underscore trade-offs between scalability, bias, and computational cost in LLM-only multilingual systems.</abstract>
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%0 Conference Proceedings
%T AI4PC-Howard University at SemEval-2026 Task 9: Evaluating Teacher-Student Weak Supervision and Direct LLM Prompting for Multilingual Political Polarization Detection
%A Aryal, Surangana
%A Aryal, Saurav
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%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 aryal-aryal-2026-ai4pc
%X We describe the AI4PC–Howard University submission to SemEval-2026 Task 9, Subtask 1 on multilingual political polarization detection across 22 languages. We investigated two approaches: (1) a weakly supervised teacher–student framework in which a large language model (LLM) generated pseudo-labels to train an XLM-RoBERTa-base classifier, and (2) (2) a context-engineered prompt-based approach using Meta-Llama-3.1-8B-Instruct. The teacher–student approach exhibited instability under distribution shift and collapsed toward majority predictions at test time. Consequently, our final submission used direct inference with Meta-Llama-3.1-8B-Instruct. While this approach produced competitive macro-F1 across evaluated languages, results reveal strong positive-class bias and substantial precision–recall imbalance. Our findings highlight limitations of weak supervision for subjective political tasks and underscore trade-offs between scalability, bias, and computational cost in LLM-only multilingual systems.
%U https://aclanthology.org/2026.semeval-1.316/
%P 2506-2511

Markdown (Informal)

[AI4PC-Howard University at SemEval-2026 Task 9: Evaluating Teacher-Student Weak Supervision and Direct LLM Prompting for Multilingual Political Polarization Detection](https://aclanthology.org/2026.semeval-1.316/) (Aryal & Aryal, SemEval 2026)

• AI4PC-Howard University at SemEval-2026 Task 9: Evaluating Teacher-Student Weak Supervision and Direct LLM Prompting for Multilingual Political Polarization Detection (Aryal & Aryal, SemEval 2026)

ACL

• Surangana Aryal and Saurav Aryal. 2026. AI4PC-Howard University at SemEval-2026 Task 9: Evaluating Teacher-Student Weak Supervision and Direct LLM Prompting for Multilingual Political Polarization Detection. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 2506–2511, San Diego, California, USA. Association for Computational Linguistics.