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

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2024-11-27 06:30:03

Large Language Models (LLMs) have revolutionized how we interact with and build conversational AI systems. While these models demonstrate impressive capabilities out of the box in general conversation, organizations face significant challenges when attempting to apply them to domain-specific business contexts.

Fine-tuning offers a solution to these challenges by allowing organizations to adapt off-the-shelf open models to their specific needs. Unlike pre-training, which involves processing vast amounts of low-quality general data, fine-tuning an already instruction-finetuned model is a more focused process that requires a much smaller, higher-quality labeled dataset of domain-specific examples.

In this article, we’ll talk specifically about multi-turn fine-tuning, whereby we can teach the model to maintain context across multiple exchanges while adhering to specific conversation patterns. The process helps models handle domain-specific queries with greater accuracy and ensures they respect guardrails that may be unique to your business context. This multi-turn capability is especially critical in scenarios like customer service, technical support, or complex multi-hop task completion, where a single exchange is rarely sufficient to address the user's needs.

Another practical example of multi-turn finetuning is the multi-turn function calling workflow. If you need an LLM to solve complex problems by using tools, you will need to train it to identify which sequence of tools to use one after the other and make decisions depending on the information obtained from the intermediate tool usage.