@inproceedings{baothman-2026-argtune,
title = "{A}rg{T}une at {AISA}-{A}rabic{FC}: A Modular Pipeline for {A}rabic Function Calling",
author = "Baothman, Sabah Abdulgader",
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.206/",
pages = "1597--1602",
ISBN = "979-8-89176-497-2",
abstract = "We present ArgTune, a modular pipeline for Arabic function calling developed for the AISA-ArabicFC shared task. ArgTune separates tool selection from argument extraction using three LoRA-fine-tuned AISA-AR-FunctionCall-Think (270M) models: a tool-selection model, a general argument model, and a specialist model for seven challenging tools, allowing each model to focus on a more specific task. Targeted augmentation, data balancing, and query-grounded post-processing further address difficult argument cases. On the official test set, ArgTune achieved 0.9964 FnAcc, 0.8506 ArgEM, and an overall score of 0.9089, ranking 8th. The results highlight the remaining difficulty of exact argument extraction despite highly accurate tool selection."
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<abstract>We present ArgTune, a modular pipeline for Arabic function calling developed for the AISA-ArabicFC shared task. ArgTune separates tool selection from argument extraction using three LoRA-fine-tuned AISA-AR-FunctionCall-Think (270M) models: a tool-selection model, a general argument model, and a specialist model for seven challenging tools, allowing each model to focus on a more specific task. Targeted augmentation, data balancing, and query-grounded post-processing further address difficult argument cases. On the official test set, ArgTune achieved 0.9964 FnAcc, 0.8506 ArgEM, and an overall score of 0.9089, ranking 8th. The results highlight the remaining difficulty of exact argument extraction despite highly accurate tool selection.</abstract>
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%0 Conference Proceedings
%T ArgTune at AISA-ArabicFC: A Modular Pipeline for Arabic Function Calling
%A Baothman, Sabah Abdulgader
%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 baothman-2026-argtune
%X We present ArgTune, a modular pipeline for Arabic function calling developed for the AISA-ArabicFC shared task. ArgTune separates tool selection from argument extraction using three LoRA-fine-tuned AISA-AR-FunctionCall-Think (270M) models: a tool-selection model, a general argument model, and a specialist model for seven challenging tools, allowing each model to focus on a more specific task. Targeted augmentation, data balancing, and query-grounded post-processing further address difficult argument cases. On the official test set, ArgTune achieved 0.9964 FnAcc, 0.8506 ArgEM, and an overall score of 0.9089, ranking 8th. The results highlight the remaining difficulty of exact argument extraction despite highly accurate tool selection.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.206/
%P 1597-1602
Markdown (Informal)
[ArgTune at AISA-ArabicFC: A Modular Pipeline for Arabic Function Calling](https://aclanthology.org/2026.arabicnlp-sharedtasks.206/) (Baothman, ArabicNLP 2026)
ACL