研究人员开发了 EmbGen，一个用于生成合成训练数据的新管道，以使较小的语言模型适应专业领域。该方法将语料库分解为实体-描述对，基于语义相似性重组它们，然后创建问答对。EmbGen 旨在克服现有合成数据生成技术的局限性，这些技术可能产生同质化输出并未能捕捉复杂依赖关系。 AI
影响 该方法可以显著降低微调小型语言模型以适应小众应用的成本和精力。
排序理由 该集群描述了一篇研究论文中提出的一种新的合成数据生成管道。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
Adapting small instruction-tuned models to specialized domains often relies on supervised fine-tuning (SFT) on curated instruction-response examples, which is expensive to collect at scale. Synthetic training examples generated by a teacher LLM from a domain corpus can reduce thi…
Adapting small instruction-tuned models to specialized domains often relies on supervised fine-tuning (SFT) on curated instruction-response examples, which is expensive to collect at scale. Synthetic training examples generated by a teacher LLM from a domain corpus can reduce thi…