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Lingo_Research_Group evaluates prompt variants for polarization detection

By PulseAugur Editorial · [2 sources] ·

Researchers from Lingo_Research_Group have detailed their approach for SemEval-2026 Task 9, focusing on multilingual polarization detection. Their study evaluated twelve different prompt designs across three subtasks using the aya-101 and Gemma3-27B models. While effective for coarse-grained polarization detection, the prompt-based methods showed limitations with more nuanced, fine-grained, and multi-label classification tasks. AI

IMPACT Prompt engineering techniques show promise for polarization detection but require further refinement for complex linguistic tasks.

RANK_REASON The cluster contains an academic paper detailing research methodology and results.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Lingo_Research_Group evaluates prompt variants for polarization detection

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Pritam Kadasi, Anuj Tiwari, Mayank Singh ·

    Lingo_Research_Group at SemEval-2026 Task 9: Evaluating Prompt Variants for Polarization Detection

    arXiv:2606.03334v1 Announce Type: new Abstract: Our submission presented in this paper is for SemEval-2026 Task 9: Multilingual Text Classification Challenge - Polarization Detection and it covers all three subtasks: (1) binary polarization detection, (2) polarization type classi…

  2. arXiv cs.CL TIER_1 English(EN) · Mayank Singh ·

    Lingo_Research_Group at SemEval-2026 Task 9: Evaluating Prompt Variants for Polarization Detection

    Our submission presented in this paper is for SemEval-2026 Task 9: Multilingual Text Classification Challenge - Polarization Detection and it covers all three subtasks: (1) binary polarization detection, (2) polarization type classification and (3) polarization manifestation iden…

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