Work

ADAPT–MTU HAI at PalmX 2025: Leveraging Full and Parameter‐Efficient LLM Fine‐Tuning for Arabic Cultural QA

Year: 2025

Type: conference-paper

Abstract: We present ADAPT-MTU HAI's submission to PalmX 2025, targeting Arabic cultural question answering through large language model (LLM) adaptation.We apply full fine-tuning on NileChat-3B for general cultural comprehension, and parameter-efficient LoRA-based tuning on ALLaM-7B for Islamic knowledge reasoning.Our models achieved first place in the General Culture subtask and third place in the Islamic Culture subtask.This paper outlines our methodology and results, demonstrating the effectiveness of aligning LLM fine-tuning strategies with cultural knowledge domains.

Source: Arabic Natural Language Processing Workshop (WANLP)

DOI: 10.18653/v1/2025.arabicnlp-sharedtasks.110

Authors: Shehenaz Hossain, Haithem Afli

Institution: Munster Technological University

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Munster Technological University, European Regional Development Fund, ADAPT - Centre for Digital Content Technology

Awards: G2399253094, G3251807