微调小模型生成低代码工作流，效果比提示大模型好10%。
Fine-Tune an SLM or Prompt an LLM? The Case of Generating Low-Code Workflows
- 微调小型语言模型生成结构化输出
- 相比提示大模型，质量提升平均10%
- 适合需要高精度结构化输出的开发者
大型语言模型（如GPT-4o）通过恰当提示可处理多种复杂任务。随着令牌成本降低，微调小型语言模型（SLMs）在实际应用中的优势——更快推理、更低开销——可能不再明显。本文针对需生成结构化输出的领域特定任务，比较了微调SLM与提示LLM在生成低代码工作流（JSON格式）上的表现。结果表明，尽管优质提示可获得合理结果，但微调使生成质量平均提升10%。我们还进行了系统性错误分析，揭示模型在结构一致性与语义准确性方面的局限。
原文摘要 · Abstract (English)
Large Language Models (LLMs) such as GPT-4o can handle a wide range of complex tasks with the right prompt. As per token costs are reduced, the advantages of fine-tuning Small Language Models (SLMs) for real-world applications -- faster inference, lower costs -- may no longer be clear. In this work, we present evidence that, for domain-specific tasks that require structured outputs, SLMs still have a quality advantage. We compare fine-tuning an SLM against prompting LLMs on the task of generating low-code workflows in JSON form. We observe that while a good prompt can yield reasonable results, fine-tuning improves quality by 10% on average. We also perform systematic error analysis to reveal model limitations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。