@inproceedings{alam-etal-2026-arguard,
title = "{A}r{G}uard Shared Task: Harmful Content Detection in {A}rabic Memes and {LLM} Prompts",
author = "Alam, Firoj and
Biswas, Md. Rafiul and
Kmainasi, Mohamed Bayan and
Shahroor, Ali Ezzat and
Mubarak, Hamdy and
Mikros, George and
Hasnat, Abul and
Zaghouani, Wajdi",
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.39/",
pages = "313--325",
ISBN = "979-8-89176-497-2",
abstract = "Harmful content appears in diverse forms, ranging from multimodal memes targeting protected groups to textual prompts designed to elicit unsafe responses from large language models. However, existing Arabic resources typically study these settings separately and often rely on coarse-grained labels. We introduce **ArGuard**, a shared task on harmful content detection in Arabic memes and LLM prompts. ArGuard consists of two tracks: Track A focuses on multimodal hate detection in Arabic memes, while Track B addresses harmful prompt detection for Arabic LLM safety evaluation. Overall, 58 teams registered for the shared task, 35 participated in the final evaluation phase, and 27 submitted system-description papers. Participating systems explored a range of approaches, including fine-tuning encoder- and decoder-based models such as AraBERT, Jais, and Qwen3-VL. The best-performing systems achieved macro-F1 scores of 0.823 on Subtask A1, 0.419 on Subtask A2, 0.984 on Subtask B1, and 0.790 on Subtask B2. These results highlight fine-grained meme classification (Subtask A2) as the most challenging setting, partly due to sparse labels and train-test distribution shifts. To facilitate further research on Arabic harmful-content detection, we release the task datasets and associated resources through the shared-task website."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="alam-etal-2026-arguard">
<titleInfo>
<title>ArGuard Shared Task: Harmful Content Detection in Arabic Memes and LLM Prompts</title>
</titleInfo>
<name type="personal">
<namePart type="given">Firoj</namePart>
<namePart type="family">Alam</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Md.</namePart>
<namePart type="given">Rafiul</namePart>
<namePart type="family">Biswas</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mohamed</namePart>
<namePart type="given">Bayan</namePart>
<namePart type="family">Kmainasi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ali</namePart>
<namePart type="given">Ezzat</namePart>
<namePart type="family">Shahroor</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hamdy</namePart>
<namePart type="family">Mubarak</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">George</namePart>
<namePart type="family">Mikros</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Abul</namePart>
<namePart type="family">Hasnat</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wajdi</namePart>
<namePart type="family">Zaghouani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks</title>
</titleInfo>
<name type="personal">
<namePart type="given">Muhammad</namePart>
<namePart type="family">Abdul-Mageed</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tamer</namePart>
<namePart type="family">Elsayed</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Houda</namePart>
<namePart type="family">Bouamor</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Samhaa</namePart>
<namePart type="family">El-Beltagy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hend</namePart>
<namePart type="family">Al-Khalifa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ahmed</namePart>
<namePart type="family">Abdelali</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">AbdelRahim</namePart>
<namePart type="family">Elmadany</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Abdellah</namePart>
<namePart type="family">El Mekki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kareem</namePart>
<namePart type="family">Darwish</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sakhar</namePart>
<namePart type="family">Alkhereyf</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hamzah</namePart>
<namePart type="family">Luqman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Salam</namePart>
<namePart type="family">Khalifa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Samar</namePart>
<namePart type="family">Magdy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zaid</namePart>
<namePart type="family">Alyafeai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Injy</namePart>
<namePart type="family">Hamed</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wajdi</namePart>
<namePart type="family">Zaghouani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Imed</namePart>
<namePart type="family">Zitouni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-497-2</identifier>
</relatedItem>
<abstract>Harmful content appears in diverse forms, ranging from multimodal memes targeting protected groups to textual prompts designed to elicit unsafe responses from large language models. However, existing Arabic resources typically study these settings separately and often rely on coarse-grained labels. We introduce **ArGuard**, a shared task on harmful content detection in Arabic memes and LLM prompts. ArGuard consists of two tracks: Track A focuses on multimodal hate detection in Arabic memes, while Track B addresses harmful prompt detection for Arabic LLM safety evaluation. Overall, 58 teams registered for the shared task, 35 participated in the final evaluation phase, and 27 submitted system-description papers. Participating systems explored a range of approaches, including fine-tuning encoder- and decoder-based models such as AraBERT, Jais, and Qwen3-VL. The best-performing systems achieved macro-F1 scores of 0.823 on Subtask A1, 0.419 on Subtask A2, 0.984 on Subtask B1, and 0.790 on Subtask B2. These results highlight fine-grained meme classification (Subtask A2) as the most challenging setting, partly due to sparse labels and train-test distribution shifts. To facilitate further research on Arabic harmful-content detection, we release the task datasets and associated resources through the shared-task website.</abstract>
<identifier type="citekey">alam-etal-2026-arguard</identifier>
<location>
<url>https://aclanthology.org/2026.arabicnlp-sharedtasks.39/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>313</start>
<end>325</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T ArGuard Shared Task: Harmful Content Detection in Arabic Memes and LLM Prompts
%A Alam, Firoj
%A Biswas, Md. Rafiul
%A Kmainasi, Mohamed Bayan
%A Shahroor, Ali Ezzat
%A Mubarak, Hamdy
%A Mikros, George
%A Hasnat, Abul
%A Zaghouani, Wajdi
%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 alam-etal-2026-arguard
%X Harmful content appears in diverse forms, ranging from multimodal memes targeting protected groups to textual prompts designed to elicit unsafe responses from large language models. However, existing Arabic resources typically study these settings separately and often rely on coarse-grained labels. We introduce **ArGuard**, a shared task on harmful content detection in Arabic memes and LLM prompts. ArGuard consists of two tracks: Track A focuses on multimodal hate detection in Arabic memes, while Track B addresses harmful prompt detection for Arabic LLM safety evaluation. Overall, 58 teams registered for the shared task, 35 participated in the final evaluation phase, and 27 submitted system-description papers. Participating systems explored a range of approaches, including fine-tuning encoder- and decoder-based models such as AraBERT, Jais, and Qwen3-VL. The best-performing systems achieved macro-F1 scores of 0.823 on Subtask A1, 0.419 on Subtask A2, 0.984 on Subtask B1, and 0.790 on Subtask B2. These results highlight fine-grained meme classification (Subtask A2) as the most challenging setting, partly due to sparse labels and train-test distribution shifts. To facilitate further research on Arabic harmful-content detection, we release the task datasets and associated resources through the shared-task website.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.39/
%P 313-325
Markdown (Informal)
[ArGuard Shared Task: Harmful Content Detection in Arabic Memes and LLM Prompts](https://aclanthology.org/2026.arabicnlp-sharedtasks.39/) (Alam et al., ArabicNLP 2026)
ACL
- Firoj Alam, Md. Rafiul Biswas, Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Hamdy Mubarak, George Mikros, Abul Hasnat, and Wajdi Zaghouani. 2026. ArGuard Shared Task: Harmful Content Detection in Arabic Memes and LLM Prompts. In Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks, pages 313–325, Budapest, Hungary. Association for Computational Linguistics.