@inproceedings{alowjan-etal-2026-optimal,
title = "Optimal at {AISA}-{A}rabic{FC}: A Hybrid Approach to {A}rabic Function Calling with {PEFT} Large Language Models and Deterministic Post-Processing",
author = "Alowjan, Sarah and
Al mana, Lujain and
Alwizrah, Haya Buayyan",
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.207/",
pages = "1603--1607",
ISBN = "979-8-89176-497-2",
abstract = "Arabic function calling requires models to identify appropriate tools and reproduce arguments in the exact representation expected by evaluators. This is challenging due to dialectal variation, lexical alternatives, and surface-form normalization. We present a hybrid system based on Qwen2.5-7B-Instruct adapted with 4-bit QLoRA, followed by conservative deterministic post-processing. The neural model generates flexible tool calls, while a lightweight deterministic layer parses, validates, and casts the output to improve compliance with the exact-match evaluation format. On the official blind evaluation, our system obtains 93.24{\%} Function Accuracy and 58.38{\%} Argument Exact Match (Track A: 72.32{\%}, Track B: 71.37{\%}). Error analysis indicates that many remaining failures arise from argument-surface mismatches rather than function-selection errors, supporting a design where generative modeling handles semantics while deterministic processing controls exact realization."
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<abstract>Arabic function calling requires models to identify appropriate tools and reproduce arguments in the exact representation expected by evaluators. This is challenging due to dialectal variation, lexical alternatives, and surface-form normalization. We present a hybrid system based on Qwen2.5-7B-Instruct adapted with 4-bit QLoRA, followed by conservative deterministic post-processing. The neural model generates flexible tool calls, while a lightweight deterministic layer parses, validates, and casts the output to improve compliance with the exact-match evaluation format. On the official blind evaluation, our system obtains 93.24% Function Accuracy and 58.38% Argument Exact Match (Track A: 72.32%, Track B: 71.37%). Error analysis indicates that many remaining failures arise from argument-surface mismatches rather than function-selection errors, supporting a design where generative modeling handles semantics while deterministic processing controls exact realization.</abstract>
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%0 Conference Proceedings
%T Optimal at AISA-ArabicFC: A Hybrid Approach to Arabic Function Calling with PEFT Large Language Models and Deterministic Post-Processing
%A Alowjan, Sarah
%A Al mana, Lujain
%A Alwizrah, Haya Buayyan
%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 alowjan-etal-2026-optimal
%X Arabic function calling requires models to identify appropriate tools and reproduce arguments in the exact representation expected by evaluators. This is challenging due to dialectal variation, lexical alternatives, and surface-form normalization. We present a hybrid system based on Qwen2.5-7B-Instruct adapted with 4-bit QLoRA, followed by conservative deterministic post-processing. The neural model generates flexible tool calls, while a lightweight deterministic layer parses, validates, and casts the output to improve compliance with the exact-match evaluation format. On the official blind evaluation, our system obtains 93.24% Function Accuracy and 58.38% Argument Exact Match (Track A: 72.32%, Track B: 71.37%). Error analysis indicates that many remaining failures arise from argument-surface mismatches rather than function-selection errors, supporting a design where generative modeling handles semantics while deterministic processing controls exact realization.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.207/
%P 1603-1607
Markdown (Informal)
[Optimal at AISA-ArabicFC: A Hybrid Approach to Arabic Function Calling with PEFT Large Language Models and Deterministic Post-Processing](https://aclanthology.org/2026.arabicnlp-sharedtasks.207/) (Alowjan et al., ArabicNLP 2026)
ACL