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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2601.17364 |
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| _version_ | 1866911396280664064 |
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| author | Fasha, Mohammed Hammo, Bassam Sowan, Bilal Barham, Husam Nsour, Esam |
| author_facet | Fasha, Mohammed Hammo, Bassam Sowan, Bilal Barham, Husam Nsour, Esam |
| contents | This study uses Jordanian law as a case study to explore the fine-tuning of the Llama-3.1 large language model for Arabic question-answering. Two versions of the model - Llama-3.1-8B-bnb-4bit and Llama-3.1-8B-Instruct-bnb-4bit - were fine-tuned using parameter-efficient fine-tuning (PEFT) with LoRA adapters and 4-bit quantized models, leveraging the Unsloth framework for accelerated and resource-efficient training. A custom dataset of 6000 legal question-answer pairs was curated from Jordanian laws and formatted into structured prompts. Performance was evaluated using the BLEU and the ROUGE metrics to compare the fine-tuned models to their respective base versions. Results demonstrated improved legal reasoning and accuracy while achieving resource efficiency through quantization and optimized fine-tuning strategies. This work underscores the potential of adapting large language models for Arabic legal domains and highlights effective techniques for fine-tuning domain-specific tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17364 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Parameter Efficient Fine Tuning Llama 3.1 for Answering Arabic Legal Questions: A Case Study on Jordanian Laws Fasha, Mohammed Hammo, Bassam Sowan, Bilal Barham, Husam Nsour, Esam Computation and Language Artificial Intelligence This study uses Jordanian law as a case study to explore the fine-tuning of the Llama-3.1 large language model for Arabic question-answering. Two versions of the model - Llama-3.1-8B-bnb-4bit and Llama-3.1-8B-Instruct-bnb-4bit - were fine-tuned using parameter-efficient fine-tuning (PEFT) with LoRA adapters and 4-bit quantized models, leveraging the Unsloth framework for accelerated and resource-efficient training. A custom dataset of 6000 legal question-answer pairs was curated from Jordanian laws and formatted into structured prompts. Performance was evaluated using the BLEU and the ROUGE metrics to compare the fine-tuned models to their respective base versions. Results demonstrated improved legal reasoning and accuracy while achieving resource efficiency through quantization and optimized fine-tuning strategies. This work underscores the potential of adapting large language models for Arabic legal domains and highlights effective techniques for fine-tuning domain-specific tasks. |
| title | Parameter Efficient Fine Tuning Llama 3.1 for Answering Arabic Legal Questions: A Case Study on Jordanian Laws |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2601.17364 |