@inproceedings{lipinski-makarov-2026-imak,
title = "i{M}ak {AI} Lab at {A}r{G}uard Shared Tasks: Heterogeneous {L}o{RA} Ensembles of Vision-Language Models for {A}rabic Hateful Meme Detection",
author = "Lipinski, Artiom and
Makarov, Ilya",
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.57/",
pages = "443--447",
ISBN = "979-8-89176-497-2",
abstract = "We describe our submissions to Task A of the ArGuard 2026 shared task on harmful-content detection in Arabic memes: binary hateful-meme detection (Subtask A1) and fine-grained multilabel categorization (Subtask A2). Our A1 system is a probability-averaging ensemble of 22 fine-tuned models: 18 Qwen3-VL vision-language members (8B and 32B) trained with LoRA and scored via first-token logits, and 4 Arabic text-encoder members (MARBERTv2, AraBERTv02-twitter), combined at a fixed 0.85/0.15 modality mix. The system ranked first on Subtask A1 with 0.8234 macro F1, 2.2 points above our best single member. For A2, six LoRA-tuned VLMs with sigmoid multilabel heads, an A1-consistency mask, and flat 0.5 thresholds scored 0.3900 macro F1. Post-hoc analysis on the released gold labels shows that our single global threshold survived an unannounced 8-point drop in the share of hateful memes in the test set, and that most remaining errors in both subtasks separate benign sarcasm or humor from mockery aimed at a protected target."
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<abstract>We describe our submissions to Task A of the ArGuard 2026 shared task on harmful-content detection in Arabic memes: binary hateful-meme detection (Subtask A1) and fine-grained multilabel categorization (Subtask A2). Our A1 system is a probability-averaging ensemble of 22 fine-tuned models: 18 Qwen3-VL vision-language members (8B and 32B) trained with LoRA and scored via first-token logits, and 4 Arabic text-encoder members (MARBERTv2, AraBERTv02-twitter), combined at a fixed 0.85/0.15 modality mix. The system ranked first on Subtask A1 with 0.8234 macro F1, 2.2 points above our best single member. For A2, six LoRA-tuned VLMs with sigmoid multilabel heads, an A1-consistency mask, and flat 0.5 thresholds scored 0.3900 macro F1. Post-hoc analysis on the released gold labels shows that our single global threshold survived an unannounced 8-point drop in the share of hateful memes in the test set, and that most remaining errors in both subtasks separate benign sarcasm or humor from mockery aimed at a protected target.</abstract>
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%0 Conference Proceedings
%T iMak AI Lab at ArGuard Shared Tasks: Heterogeneous LoRA Ensembles of Vision-Language Models for Arabic Hateful Meme Detection
%A Lipinski, Artiom
%A Makarov, Ilya
%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 lipinski-makarov-2026-imak
%X We describe our submissions to Task A of the ArGuard 2026 shared task on harmful-content detection in Arabic memes: binary hateful-meme detection (Subtask A1) and fine-grained multilabel categorization (Subtask A2). Our A1 system is a probability-averaging ensemble of 22 fine-tuned models: 18 Qwen3-VL vision-language members (8B and 32B) trained with LoRA and scored via first-token logits, and 4 Arabic text-encoder members (MARBERTv2, AraBERTv02-twitter), combined at a fixed 0.85/0.15 modality mix. The system ranked first on Subtask A1 with 0.8234 macro F1, 2.2 points above our best single member. For A2, six LoRA-tuned VLMs with sigmoid multilabel heads, an A1-consistency mask, and flat 0.5 thresholds scored 0.3900 macro F1. Post-hoc analysis on the released gold labels shows that our single global threshold survived an unannounced 8-point drop in the share of hateful memes in the test set, and that most remaining errors in both subtasks separate benign sarcasm or humor from mockery aimed at a protected target.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.57/
%P 443-447
Markdown (Informal)
[iMak AI Lab at ArGuard Shared Tasks: Heterogeneous LoRA Ensembles of Vision-Language Models for Arabic Hateful Meme Detection](https://aclanthology.org/2026.arabicnlp-sharedtasks.57/) (Lipinski & Makarov, ArabicNLP 2026)
ACL