多轮对话微调 LLM 的完整指南：数据集准备与损失掩码策略
原标题：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
AI 摘要
Together AI 发布了一篇关于多轮对话微调 LLM 的教程，指出通用模型在领域适应、知识限制和多轮复杂性方面存在不足，而微调可解决这些问题。文章详细介绍了数据集准备、损失掩码策略（包括无掩码、全掩码和样板掩码）以及微调 Llama 8B 的实践，并引用了相关研究，强调掩码策略的选择取决于数据集特性。
正文节选
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. Despite their broad capabilities, general-purpose LLMs face several key limitations: - Domain Adaptation: Organizations often struggle with getting LLMs to understand their unique data for