@inproceedings{kannan-etal-2026-srk,
title = "{SRK} {AI} Lab at {S}tance{E}val-2026: Multi-{LLM} Few-Shot Prompting with Evidence-Guided Auditing for {A}rabic Stance Detection",
author = "Kannan, V. T. Rushi and
Renjit, Sara and
Tekchandani, Dr. Hitesh",
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.72/",
pages = "552--567",
ISBN = "979-8-89176-497-2",
abstract = "This paper presents the SRK AI Lab system for Arabic stance detection in the StanceEval 2026 shared task using the Mawqif-XT benchmark. We employ a few-shot multi-LLM framework with GPT-5.5 Instant, Gemini 3.6 Flash with Extended Thinking, and Qwen 3.7 Plus to classify target{--}post pairs as Favor, Against, or None. The models independently predict stance, followed by GPT-5.5 Instant auditing based on target{--}post relation, linguistic context, and stance evidence. The audit results are combined with cross-model comparison to select the final label. Our system achieves an official Favg2 of 0.8420 in Track 1 and 0.9192 in Track 2, ranking 8th and 3rd, respectively. These results demonstrate the performance of multi-LLM few-shot prompting with GPT-5.5 auditing for Arabic stance detection."
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<abstract>This paper presents the SRK AI Lab system for Arabic stance detection in the StanceEval 2026 shared task using the Mawqif-XT benchmark. We employ a few-shot multi-LLM framework with GPT-5.5 Instant, Gemini 3.6 Flash with Extended Thinking, and Qwen 3.7 Plus to classify target–post pairs as Favor, Against, or None. The models independently predict stance, followed by GPT-5.5 Instant auditing based on target–post relation, linguistic context, and stance evidence. The audit results are combined with cross-model comparison to select the final label. Our system achieves an official Favg2 of 0.8420 in Track 1 and 0.9192 in Track 2, ranking 8th and 3rd, respectively. These results demonstrate the performance of multi-LLM few-shot prompting with GPT-5.5 auditing for Arabic stance detection.</abstract>
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%0 Conference Proceedings
%T SRK AI Lab at StanceEval-2026: Multi-LLM Few-Shot Prompting with Evidence-Guided Auditing for Arabic Stance Detection
%A Kannan, V. T. Rushi
%A Renjit, Sara
%A Tekchandani, Dr. Hitesh
%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 kannan-etal-2026-srk
%X This paper presents the SRK AI Lab system for Arabic stance detection in the StanceEval 2026 shared task using the Mawqif-XT benchmark. We employ a few-shot multi-LLM framework with GPT-5.5 Instant, Gemini 3.6 Flash with Extended Thinking, and Qwen 3.7 Plus to classify target–post pairs as Favor, Against, or None. The models independently predict stance, followed by GPT-5.5 Instant auditing based on target–post relation, linguistic context, and stance evidence. The audit results are combined with cross-model comparison to select the final label. Our system achieves an official Favg2 of 0.8420 in Track 1 and 0.9192 in Track 2, ranking 8th and 3rd, respectively. These results demonstrate the performance of multi-LLM few-shot prompting with GPT-5.5 auditing for Arabic stance detection.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.72/
%P 552-567
Markdown (Informal)
[SRK AI Lab at StanceEval-2026: Multi-LLM Few-Shot Prompting with Evidence-Guided Auditing for Arabic Stance Detection](https://aclanthology.org/2026.arabicnlp-sharedtasks.72/) (Kannan et al., ArabicNLP 2026)
ACL