@inproceedings{naz-2026-rabia,
title = "rabia{\_}naz at {S}tance{E}val-2026: An Ensemble of Fine-Tuned Open {LLM}s for {A}rabic Stance Detection on Seen Targets",
author = "Naz, Rabia",
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.70/",
pages = "538--543",
ISBN = "979-8-89176-497-2",
abstract = "This paper presents a system for StanceEval-2026 Track 1, Arabic stance detection on seen targets. Systems in this track are trained on the provided targets and evaluated on a related target within the same thematic domain. Open large language models are fine-tuned with QLoRA on the original Mawqif training data and adapted to the test target through transductive self-training, in which unlabeled test tweets are added to training only when independent models agree on their label. The final prediction is a majority vote over three fine-tuned models, Qwen2.5-7B, the Arabic-native ALLaM-7B, and a sarcasm-aware variant that predicts the official sarcasm label before the stance label. The system scored 0.7564 Favg2 and placed 12th of 21 teams on the official leaderboard. Across seventeen scored variants, task fine-tuning outperformed zero-shot prompting at every model scale, heterogeneous fine-tuned models converged on 98{\%} of test predictions, and further gains required decorrelated ensemble members. The sarcasm annotation supplied this decorrelation and improved the vote, while the sentiment annotation degraded it."
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<abstract>This paper presents a system for StanceEval-2026 Track 1, Arabic stance detection on seen targets. Systems in this track are trained on the provided targets and evaluated on a related target within the same thematic domain. Open large language models are fine-tuned with QLoRA on the original Mawqif training data and adapted to the test target through transductive self-training, in which unlabeled test tweets are added to training only when independent models agree on their label. The final prediction is a majority vote over three fine-tuned models, Qwen2.5-7B, the Arabic-native ALLaM-7B, and a sarcasm-aware variant that predicts the official sarcasm label before the stance label. The system scored 0.7564 Favg2 and placed 12th of 21 teams on the official leaderboard. Across seventeen scored variants, task fine-tuning outperformed zero-shot prompting at every model scale, heterogeneous fine-tuned models converged on 98% of test predictions, and further gains required decorrelated ensemble members. The sarcasm annotation supplied this decorrelation and improved the vote, while the sentiment annotation degraded it.</abstract>
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%0 Conference Proceedings
%T rabia_naz at StanceEval-2026: An Ensemble of Fine-Tuned Open LLMs for Arabic Stance Detection on Seen Targets
%A Naz, Rabia
%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 naz-2026-rabia
%X This paper presents a system for StanceEval-2026 Track 1, Arabic stance detection on seen targets. Systems in this track are trained on the provided targets and evaluated on a related target within the same thematic domain. Open large language models are fine-tuned with QLoRA on the original Mawqif training data and adapted to the test target through transductive self-training, in which unlabeled test tweets are added to training only when independent models agree on their label. The final prediction is a majority vote over three fine-tuned models, Qwen2.5-7B, the Arabic-native ALLaM-7B, and a sarcasm-aware variant that predicts the official sarcasm label before the stance label. The system scored 0.7564 Favg2 and placed 12th of 21 teams on the official leaderboard. Across seventeen scored variants, task fine-tuning outperformed zero-shot prompting at every model scale, heterogeneous fine-tuned models converged on 98% of test predictions, and further gains required decorrelated ensemble members. The sarcasm annotation supplied this decorrelation and improved the vote, while the sentiment annotation degraded it.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.70/
%P 538-543
Markdown (Informal)
[rabia_naz at StanceEval-2026: An Ensemble of Fine-Tuned Open LLMs for Arabic Stance Detection on Seen Targets](https://aclanthology.org/2026.arabicnlp-sharedtasks.70/) (Naz, ArabicNLP 2026)
ACL