@inproceedings{altamirano-etal-2026-servsocia,
title = "{S}erv{S}oc{IA} at {S}emeval-2026 Task 9: Evaluating Prompt Strategies for Polarization Detection",
author = "Altamirano, Jacob and
Leon P{\'e}rez, Mario and
Ruiz-Juarez, Bruno and
Chiruzzo, Luis and
Gomez-Adorno, Helena and
Balouchzahi, Fazlourrahman",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.346/",
pages = "2754--2759",
ISBN = "979-8-89176-414-9",
abstract = "This paper presents our approach to Subtask 1 of SemEval-2026 Task 9 on multilingual polarization detection in social media texts in English and Spanish. We model the task as a prompt-based binary classification problem and systematically compare zero-shot, one-shot, and few-shot strategies across multiple large language models accessed via commercial APIs, without task-specific fine-tuning. Our controlled experimental setup enforces strict data separation and consistent decoding conditions to analyze the impact of in-context supervision across architectures and languages. Results indicate that well-structured prompting enables competitive performance, though implicit and culturally nuanced polarization remains challenging."
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<abstract>This paper presents our approach to Subtask 1 of SemEval-2026 Task 9 on multilingual polarization detection in social media texts in English and Spanish. We model the task as a prompt-based binary classification problem and systematically compare zero-shot, one-shot, and few-shot strategies across multiple large language models accessed via commercial APIs, without task-specific fine-tuning. Our controlled experimental setup enforces strict data separation and consistent decoding conditions to analyze the impact of in-context supervision across architectures and languages. Results indicate that well-structured prompting enables competitive performance, though implicit and culturally nuanced polarization remains challenging.</abstract>
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%0 Conference Proceedings
%T ServSocIA at Semeval-2026 Task 9: Evaluating Prompt Strategies for Polarization Detection
%A Altamirano, Jacob
%A Leon Pérez, Mario
%A Ruiz-Juarez, Bruno
%A Chiruzzo, Luis
%A Gomez-Adorno, Helena
%A Balouchzahi, Fazlourrahman
%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 altamirano-etal-2026-servsocia
%X This paper presents our approach to Subtask 1 of SemEval-2026 Task 9 on multilingual polarization detection in social media texts in English and Spanish. We model the task as a prompt-based binary classification problem and systematically compare zero-shot, one-shot, and few-shot strategies across multiple large language models accessed via commercial APIs, without task-specific fine-tuning. Our controlled experimental setup enforces strict data separation and consistent decoding conditions to analyze the impact of in-context supervision across architectures and languages. Results indicate that well-structured prompting enables competitive performance, though implicit and culturally nuanced polarization remains challenging.
%U https://aclanthology.org/2026.semeval-1.346/
%P 2754-2759
Markdown (Informal)
[ServSocIA at Semeval-2026 Task 9: Evaluating Prompt Strategies for Polarization Detection](https://aclanthology.org/2026.semeval-1.346/) (Altamirano et al., SemEval 2026)
ACL