Fine-Tuning LLMs for Multi-Turn Conversations: A Technical Deep Dive

Fine-tuning LLMs for multi-turn conversations involves adapting open models to specific business contexts, addressing challenges such as domain adaptation, knowledge constraints, and maintaining context across multiple exchanges. This process requires a smaller, high-quality labeled dataset of domain-specific examples. Multi-turn fine-tuning helps models handle domain-specific queries with greater accuracy and ensures they respect unique guardrails in business contexts. Dataset preparation is crucial for successful fine-tuning, ensuring proper conversation structure, clear turn delineation, system messages to set the context, consistent role labeling, and JSONL format compatibility. Loss masking in instruction fine-tuning refers to selectively including or excluding certain parts of input when computing training loss, with three approaches: no instruction masking, full instruction masking, and boilerplate masking. Recent research suggests that not masking instructions often leads to better model performance compared to the traditional approach. Fine-tuning LLMs for multi-turn conversations requires careful attention to dataset preparation, training implementation, and evaluation, with optimal results achieved by starting with high-quality conversation data, proper input masking, using parameter-efficient fine-tuning methods, and monitoring and evaluating throughout the process.