@inproceedings{alshehri-aljubayri-2026-arabic,
title = "{A}rabic Function Forge at {AISA}-{A}rabic{FC}: Copy, Don{'}t Interpret {---} Training-Free {A}rabic Function Calling",
author = "Alshehri, Anfal and
Aljubayri, Jumanah",
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.212/",
pages = "1654--1657",
ISBN = "979-8-89176-497-2",
abstract = "Arabic Function Forge is our submission to AISA-ArabicFC 2026. Our system is training-free: it uses a single off-the-shelf open-weights model, prompted once per request, with no parameter updates. It placed fifth on Track A, fourth on Track B, and sixth on Track C. Our central observation is that function selection is nearly saturated on this benchmark while argument extraction is not: we reach 0.9893 function accuracy but only 0.8686 argument exact match, so 94.9{\%} of the remaining headroom lies in arguments. The dominant failure is not misunderstanding but over-interpretation: the model translates values, converts dates to ISO form, and adds fields the user never provided. Our system is designed to suppress this behaviour by preserving argument values as written. We further find that the dialect penalty is concentrated rather than graded, with most of the drop occurring on Maghrebi."
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<abstract>Arabic Function Forge is our submission to AISA-ArabicFC 2026. Our system is training-free: it uses a single off-the-shelf open-weights model, prompted once per request, with no parameter updates. It placed fifth on Track A, fourth on Track B, and sixth on Track C. Our central observation is that function selection is nearly saturated on this benchmark while argument extraction is not: we reach 0.9893 function accuracy but only 0.8686 argument exact match, so 94.9% of the remaining headroom lies in arguments. The dominant failure is not misunderstanding but over-interpretation: the model translates values, converts dates to ISO form, and adds fields the user never provided. Our system is designed to suppress this behaviour by preserving argument values as written. We further find that the dialect penalty is concentrated rather than graded, with most of the drop occurring on Maghrebi.</abstract>
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%0 Conference Proceedings
%T Arabic Function Forge at AISA-ArabicFC: Copy, Don’t Interpret — Training-Free Arabic Function Calling
%A Alshehri, Anfal
%A Aljubayri, Jumanah
%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 alshehri-aljubayri-2026-arabic
%X Arabic Function Forge is our submission to AISA-ArabicFC 2026. Our system is training-free: it uses a single off-the-shelf open-weights model, prompted once per request, with no parameter updates. It placed fifth on Track A, fourth on Track B, and sixth on Track C. Our central observation is that function selection is nearly saturated on this benchmark while argument extraction is not: we reach 0.9893 function accuracy but only 0.8686 argument exact match, so 94.9% of the remaining headroom lies in arguments. The dominant failure is not misunderstanding but over-interpretation: the model translates values, converts dates to ISO form, and adds fields the user never provided. Our system is designed to suppress this behaviour by preserving argument values as written. We further find that the dialect penalty is concentrated rather than graded, with most of the drop occurring on Maghrebi.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.212/
%P 1654-1657
Markdown (Informal)
[Arabic Function Forge at AISA-ArabicFC: Copy, Don’t Interpret — Training-Free Arabic Function Calling](https://aclanthology.org/2026.arabicnlp-sharedtasks.212/) (Alshehri & Aljubayri, ArabicNLP 2026)
ACL