Fine-tuning of Large Language Model (LLM)
Nilesh H. Dhannaseth, Sameer Walthare, Siddharth Supekar, Prerna Sorte, Kshitija Bais, Sayali Bhagwatkar · 2024
Fine-Tuning the Llama-2 Language Model for Enhanced Natural Language Understanding Abstract: Natural Language Processing (NLP) has witnessed significant advancements with the advent of large pre-trained language models (LLMs). Llama-2, a state-of-the-art LLM, has demonstrated remarkable capabilities in various NLP tasks. This research paper explores the fine-tuning of the Llama-2 language model to further enhance its performance in specific domains and tasks. The study begins by providing an overview of the Llama-2 architecture and its pre-training methodology. It then delves into the process of fine-tuning, outlining the steps taken to adapt the model to specific datasets and tasks. Special attention is given to avoiding overfitting and optimising hyperparameters during the fine-tuning process. To evaluate the effectiveness of the fine-tuned Llama-2 model, a series of experiments are conducted across diverse NLP benchmarks, including sentiment analysis, named entity recognition, and question answering. The results demonstrate improvements in accuracy and efficiency, showcasing the model’s adaptability and versatility. Furthermore, this paper addresses the ethical considerations associated with LLMs, such as potential biases and unintended consequences. Strategies for mitigating biases are discussed, emphasising the importance of responsible AI development. In conclusion, the fine-tuning of the Llama-2 language model proves to be a valuable approach for tailoring its capabilities to specific NLP tasks. The findings of this research contribute to the ongoing discourse on optimising LLMs for improved natural language understanding while emphasising the importance of ethical considerations in AI development.