Fine-Tuning Large Language Models with Domain-Specific Data: A Comprehensive Analysis of Parameter-Efficient Methods and Performance Optimization
Abstract: The proliferation of Large Language Models (LLMs) has transformed natural language processing, yet their general-purpose training often yields suboptimal performance in specialized domains. This paper presents a comprehensive empirical analysis of fine-tuning methodologies for adapting state-of-the-art open-source LLMs to domain-specific tasks. I have systematically evaluated full parameter fine-tuning against parameter-efficient techniques including Low-Rank Adaptation (LoRA), AdaLoRA, and QLoRA across four critical domains: medical, legal, financial, and scientific literature. Our experimental framework encompasses four representative opensource models from 2023: LLaMA-7B/13B, Falcon-7B, and MPT-7B. Results demonstrate that domain-specific fine-tuning achieves performance improvements of 18.3% to 42.7% across benchmarks, with parameter efficient methods achieving 95.2% of full fine-tuning performance while using only 0.52% of trainable parameters. Our analysis reveals optimal hyperparameter configurations, convergence patterns, and computational trade-offs, providing actionable insights for practitioners. I present a comprehensive evaluation metrics and detailed ablation studies that establish new benchmarks for domain adaptation in large-scale language models.
Keywords: Large Language Models, Fine-tuning, Domain Adaptation, Parameter-Efficient Learning, LoRA, Transfer Learning, Natural Language Processing
How to Cite?: Tharakesavulu Vangalapat, "Fine-Tuning Large Language Models with Domain-Specific Data: A Comprehensive Analysis of Parameter-Efficient Methods and Performance Optimization", Volume 13 Issue 2, February 2024, International Journal of Science and Research (IJSR), Pages: 1917-1927, https://www.ijsr.net/getabstract.php?paperid=SR24228092614, DOI: https://dx.doi.org/10.21275/SR24228092614