Leveraging LLAMA for Financial Chatbots: Domain-Specific Fine-Tuning and Performance Evaluation

Large Language Models (LLMs) have revolutionised the landscape of natural language processing (NLP), offering sophisticated conversational capabilities across various domains. This paper explores the adaptation of Meta’s LLaMA model for financial chatbot applications, emphasising domain-specific fine-tuning and performance evaluation. Fine-tuning LLaMA for finance requires specialised datasets, encompassing market trends, financial regulations, and investment strategies to enhance contextual understanding and response accuracy. Key aspects of this process include data curation, supervised fine-tuning, and reinforcement learning techniques, which aim to align model outputs with financial reasoning and industry standards. Furthermore, evaluation metrics such as perplexity, response coherence, and financial sentiment analysis are examined to gauge chatbot effectiveness. By integrating domain-specific knowledge, LLaMA-powered financial chatbots can provide users with more precise, context-aware insights, facilitating tasks such as portfolio management, risk assessment, and regulatory compliance. Advancements in retrieval-augmented generation (RAG) and model distillation further optimise performance, ensuring efficiency and reliability in financial applications. The paper also addresses ethical considerations, including bias mitigation and regulatory compliance, to promote the responsible deployment of AI in the financial services sector.