@inproceedings{khadra-etal-2026-u4rasd,
title = "{U}4{RASD} at {S}tance{E}val 2026: Fine-Tuning, Prompting, and Ensembling for Unseen-Target {A}rabic Stance Detection",
author = "Khadra, Giovanni and
Chhaytle, Mohamad and
Hamdan, Nancy and
Zaraket, Fadi",
editor = "Abdul-Mageed, Muhammad and
Elsayed, Tamer and
Bouamor, Houda and
El-Beltagy, Samhaa and
Al-Khalifa, Hend and
Abdelali, Ahmed and
Elmadany, AbdelRahim and
El Mekki, Abdellah and
Darwish, Kareem and
Alkhereyf, Sakhar and
Luqman, Hamzah and
Khalifa, Salam and
Magdy, Samar and
Alyafeai, Zaid and
Hamed, Injy and
Zaghouani, Wajdi and
Zitouni, Imed",
booktitle = "Proceedings of The Fourth {A}rabic Natural Language Processing Conference: Shared Tasks",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.arabicnlp-sharedtasks.79/",
pages = "625--635",
ISBN = "979-8-89176-497-2",
abstract = "This paper describes our system for StanceEval 2026 Track 2, which requires predicting stance toward targets not seen during training. We explored fine-tuning different Arabic BERT encoder models, using class weighting techniques, incorporating auxiliary sentiment and sarcasm tasks in sequential and parallel multi-task setups, and focal and margin-based contrastive losses. We also tried zero-shot and few-shot prompting, self-consistency with escalation, LoRA fine-tuning, and an ensemble approach. Our best-performing submission on the official blind test set was a training-free few-shot prompting approach using Gemini 2.5 Flash with an extended prompt, achieving an $F_{avg2}$ of 87.41 and an $F_{avg3}$ of 76.94, and ranking 8th out of 13 teams. This result shows that a simpler, training-free prompting approach can generalize to unseen targets more effectively than fine-tuned and ensemble systems."
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<abstract>This paper describes our system for StanceEval 2026 Track 2, which requires predicting stance toward targets not seen during training. We explored fine-tuning different Arabic BERT encoder models, using class weighting techniques, incorporating auxiliary sentiment and sarcasm tasks in sequential and parallel multi-task setups, and focal and margin-based contrastive losses. We also tried zero-shot and few-shot prompting, self-consistency with escalation, LoRA fine-tuning, and an ensemble approach. Our best-performing submission on the official blind test set was a training-free few-shot prompting approach using Gemini 2.5 Flash with an extended prompt, achieving an F_avg2 of 87.41 and an F_avg3 of 76.94, and ranking 8th out of 13 teams. This result shows that a simpler, training-free prompting approach can generalize to unseen targets more effectively than fine-tuned and ensemble systems.</abstract>
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%0 Conference Proceedings
%T U4RASD at StanceEval 2026: Fine-Tuning, Prompting, and Ensembling for Unseen-Target Arabic Stance Detection
%A Khadra, Giovanni
%A Chhaytle, Mohamad
%A Hamdan, Nancy
%A Zaraket, Fadi
%Y Abdul-Mageed, Muhammad
%Y Elsayed, Tamer
%Y Bouamor, Houda
%Y El-Beltagy, Samhaa
%Y Al-Khalifa, Hend
%Y Abdelali, Ahmed
%Y Elmadany, AbdelRahim
%Y El Mekki, Abdellah
%Y Darwish, Kareem
%Y Alkhereyf, Sakhar
%Y Luqman, Hamzah
%Y Khalifa, Salam
%Y Magdy, Samar
%Y Alyafeai, Zaid
%Y Hamed, Injy
%Y Zaghouani, Wajdi
%Y Zitouni, Imed
%S Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-497-2
%F khadra-etal-2026-u4rasd
%X This paper describes our system for StanceEval 2026 Track 2, which requires predicting stance toward targets not seen during training. We explored fine-tuning different Arabic BERT encoder models, using class weighting techniques, incorporating auxiliary sentiment and sarcasm tasks in sequential and parallel multi-task setups, and focal and margin-based contrastive losses. We also tried zero-shot and few-shot prompting, self-consistency with escalation, LoRA fine-tuning, and an ensemble approach. Our best-performing submission on the official blind test set was a training-free few-shot prompting approach using Gemini 2.5 Flash with an extended prompt, achieving an F_avg2 of 87.41 and an F_avg3 of 76.94, and ranking 8th out of 13 teams. This result shows that a simpler, training-free prompting approach can generalize to unseen targets more effectively than fine-tuned and ensemble systems.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.79/
%P 625-635
Markdown (Informal)
[U4RASD at StanceEval 2026: Fine-Tuning, Prompting, and Ensembling for Unseen-Target Arabic Stance Detection](https://aclanthology.org/2026.arabicnlp-sharedtasks.79/) (Khadra et al., ArabicNLP 2026)
ACL