Image de couverture de livre

Parameter Efficient Fine Tuning Llama 3.1 for Answering Arabic Legal Questions: A Case Study on Jordanian Laws

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Fasha, Mohammed, Hammo, Bassam, Sowan, Bilal, Barham, Husam, Nsour, Esam
Format:	Preprint
Publié:	2026
Sujets:	Computation and Language Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Formal Description of Arabic Syntactic Structure in the Framework of the Government and Binding Theory
par: Hammo Bassam
Publié: (2014)

Do LLMs Understand Romanian Driving Laws? A Study on Multimodal and Fine-Tuned Question Answering
par: Barbu, Eduard, et autres
Publié: (2025)

Question-Answering (QA) Model for a Personalized Learning Assistant for Arabic Language
par: Sammoudi, Mohammad, et autres
Publié: (2024)

Mitigating the OWASP Top 10 For Large Language Models Applications using Intelligent Agents
par: Fasha, Mohammad, et autres
Publié: (2026)

Reinforcement Learning Outperforms Supervised Fine-Tuning: A Case Study on Audio Question Answering
par: Li, Gang, et autres
Publié: (2025)

LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
par: Zheng, Yaowei, et autres
Publié: (2024)

Fine-tuning Large Language Models for Improving Factuality in Legal Question Answering
par: Hu, Yinghao, et autres
Publié: (2025)

A Grammar of Jordanian Arabic
par: Herin, Bruno
Publié: (2025)

Attention-based transformer models for image captioning across languages: An in-depth survey and evaluation
par: Albadarneh, Israa A., et autres
Publié: (2025)

Experimenting with Legal AI Solutions: The Case of Question-Answering for Access to Justice
par: Li, Jonathan, et autres
Publié: (2024)

A Llama walks into the 'Bar': Efficient Supervised Fine-Tuning for Legal Reasoning in the Multi-state Bar Exam
par: Fernandes, Rean, et autres
Publié: (2025)

Assessment of RAG and Fine-Tuning for Industrial Question-Answering-Applications
par: Sturm, Jakob, et autres
Publié: (2026)

Fine-Tuning LLMs for Reliable Medical Question-Answering Services
par: Anaissi, Ali, et autres
Publié: (2024)

Pre-trained Transformer-Based Approach for Arabic Question Answering : A Comparative Study
par: Alsubhi, Kholoud, et autres
Publié: (2021)

Measuring the Groundedness of Legal Question-Answering Systems
par: Trautmann, Dietrich, et autres
Publié: (2024)

Question Answering on Patient Medical Records with Private Fine-Tuned LLMs
par: Kothari, Sara, et autres
Publié: (2025)

60 Data Points are Sufficient to Fine-Tune LLMs for Question-Answering
par: Ye, Junjie, et autres
Publié: (2024)

Synthetic Dataset Creation and Fine-Tuning of Transformer Models for Question Answering in Serbian
par: Cvetanović, Aleksa, et autres
Publié: (2024)

Intelligent Legal Assistant: An Interactive Clarification System for Legal Question Answering
par: Yao, Rujing, et autres
Publié: (2025)

ArabicaQA: A Comprehensive Dataset for Arabic Question Answering
par: Abdallah, Abdelrahman, et autres
Publié: (2024)

Pre-training, Fine-tuning and Re-ranking: A Three-Stage Framework for Legal Question Answering
par: Ni, Shiwen, et autres
Publié: (2024)

Parameter-Efficient Fine-Tuning With Adapters
par: Chen, Keyu, et autres
Publié: (2024)

Do Clinical Question Answering Systems Really Need Specialised Medical Fine Tuning?
par: Ray, Sushant Kumar, et autres
Publié: (2026)

Evaluating Fine-Tuning Efficiency of Human-Inspired Learning Strategies in Medical Question Answering
par: Yang, Yushi, et autres
Publié: (2024)

Fine-Tuning vs. RAG for Multi-Hop Question Answering with Novel Knowledge
par: Yang, Zhuoyi, et autres
Publié: (2026)

AraS2P: Arabic Speech-to-Phonemes System
par: Matar, Bassam, et autres
Publié: (2025)

On Early Detection of Hallucinations in Factual Question Answering
par: Snyder, Ben, et autres
Publié: (2023)

Decompose-and-Refine: Structured Legal Question Answering with Parametric Retrieval
par: lee, Jihyung, et autres
Publié: (2026)

Parameter-Efficient Fine-Tuning for Medical Text Summarization: A Comparative Study of Lora, Prompt Tuning, and Full Fine-Tuning
par: Shernazarov, Ulugbek, et autres
Publié: (2026)

CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering
par: Wiratunga, Nirmalie, et autres
Publié: (2024)

A Comprehensive Study on Fine-Tuning Large Language Models for Medical Question Answering Using Classification Models and Comparative Analysis
par: Ucar, Aysegul, et autres
Publié: (2025)

MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language
par: Ali, Muhammad Asif, et autres
Publié: (2024)

MedAraBench: Large-Scale Arabic Medical Question Answering Dataset and Benchmark
par: Abu-Daoud, Mouath, et autres
Publié: (2026)

AraHealthQA 2025: The First Shared Task on Arabic Health Question Answering
par: Alhuzali, Hassan, et autres
Publié: (2025)

NitiBench: A Comprehensive Study of LLM Framework Capabilities for Thai Legal Question Answering
par: Akarajaradwong, Pawitsapak, et autres
Publié: (2025)

An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models
par: Zhou, Xiongtao, et autres
Publié: (2024)

KoBLEX: Open Legal Question Answering with Multi-hop Reasoning
par: Lee, Jihyung, et autres
Publié: (2025)

Long-Context Long-Form Question Answering for Legal Domain
par: Kulkarni, Anagha, et autres
Publié: (2026)

Parameter-Efficient Fine-Tuning for Foundation Models
par: Zhang, Dan, et autres
Publié: (2025)

SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning
par: Wen, Zhihao, et autres
Publié: (2024)