Summary of Resource-aware Arabic Llm Creation: Model Adaptation, Integration, and Multi-domain Testing, by Prakash Aryan
Resource-Aware Arabic LLM Creation: Model Adaptation, Integration, and Multi-Domain Testing
by Prakash Aryan
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel approach presented in this paper fine-tunes the Qwen2-1.5B model for Arabic language processing using Quantized Low-Rank Adaptation (QLoRA) on a system with limited 4GB VRAM. The authors detail the process of adapting this large language model to the Arabic domain, utilizing diverse datasets including Bactrian, OpenAssistant, and Wikipedia Arabic corpora. The methodology involves custom data preprocessing, model configuration, and training optimization techniques such as gradient accumulation and mixed-precision training. The approach addresses specific challenges in Arabic NLP, including morphological complexity, dialectal variations, and diacritical mark handling. Experimental results demonstrate significant performance improvements, with the final loss converging to 0.1083. Comprehensive analysis is provided for GPU memory usage, training dynamics, and model evaluation across various Arabic language tasks, including text classification, question answering, and dialect identification. The fine-tuned model exhibits robustness to input perturbations and improved handling of Arabic-specific linguistic phenomena. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make a big language model better at understanding Arabic using less computer memory. Researchers adapted the Qwen2-1.5B model to work with Arabic language data from different sources, like Wikipedia. They made some adjustments to get the model to work on a computer with only 4GB of memory. The new approach helped solve problems with Arabic words and dialects, and it worked well for tasks like classifying texts and answering questions. The results are important because they can help make language technology more accessible to people who speak Arabic. |
Keywords
» Artificial intelligence » Language model » Large language model » Low rank adaptation » Nlp » Optimization » Precision » Question answering » Text classification
