Fine-tuning LLMs for AI policy chatbots using AWS SageMaker
The present study outlines a comprehensive approach to fine-tuning Large Language Models (LLMs) for developing an AI chatbot tailored to provide information on AI policies, laws, and regulations. It compares the various fine-tuning methods namely AWS Bedrock, AWS SageMaker Jumpstart, and AWS SageMaker. It also emphasises the importance of data preparation, data augmentation and fine-tuning techniques while creating custom fine-tuning job on AWS SageMaker. The study highlights the cost-effectiveness and efficiency of smaller models like Llama 3.2-1B, demonstrating their superior performance through quantitative and qualitative evaluations.
Key challenges addressed include acquiring high-quality labeled data and the benefits of customized training methods. The solution approach involves data augmentation, model training, and evaluation, showcasing significant improvements in the fine-tuned model’s performance and reliability. The paper concludes that fine-tuning LLMs on domain-specific data enhances their ability to provide context-specific guidance, with the process being cost-effective and easily repeatable using AWS SageMaker.
