LLM Fine-tuning and Evaluation for Intelligent Chatbot
Fine-tuned the Llama-3 8B model using LoRA to improve performance for customer support tasks. Evaluated the model output via RAG and incorporated human preference studies into the training process. Focused on optimizing model answers and reducing hallucination rates in real-world settings. • Applied efficient fine-tuning strategies to adapt the LLM to a domain-specific context. • Used retrieval-augmented generation (RAG) for better answer accuracy and grounding. • Included human evaluators for systematic feedback and preference scoring. • Addressed common LLM pitfalls such as irrelevant or hallucinated information.