Unleashing the Potential of LLM in ML: Techniques for Fine-Tuning, Adaptation, and Practical Deployment with ChatGPT

Abstract

Large Language Models (LLMs), particularly those like ChatGPT, have demonstrated remarkable capabilities in various natural language processing tasks, with machine translation being one of the most significant applications. This paper explores the potential of LLMs in the field of machine translation, focusing on techniques for fine-tuning, adaptation, and practical deployment. As the demand for accurate and context-aware translation services grows, understanding how to harness the strengths of models like ChatGPT becomes essential. We delve into various fine-tuning techniques, the challenges of domain adaptation, and best practices for deploying these models in real-world applications. The findings suggest that, while LLMs have inherent advantages, careful consideration of the fine-tuning process and deployment strategies is crucial for maximizing their effectiveness in machine translation tasks.