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【第180期】LLM-AutoDiff：一个基于梯度的自动化提示工程
Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：LLM-AutoDiff: Auto-Differentiate Any LLM WorkflowSummary
The provided research introduces LLM-AutoDiff, a novel framework for automating prompt engineering for complex Large Language Model workflows. This system extends gradient-based optimization to multi-step and cyclic LLM applications by treating textual inputs as trainable parameters. LLM-AutoDiff constructs a graph representing the workflow, enabling a "backward engine" LLM to generate feedback that guides iterative prompt improvements, even across functional nodes and repeated calls. The framework incorporates techniques like selective gradient computation and two-stage validation to enhance efficiency. Experimental results demonstrate that LLM-AutoDiff outperforms existing methods in accuracy and training cost across various tasks, offering a new paradigm for scaling and automating LLM deployments.
该研究提出了 LLM-AutoDiff，一个自动化提示工程（prompt engineering）的新框架，旨在优化复杂的大型语言模型（LLM）工作流。该系统通过将文本输入视为可训练参数，将基于梯度的优化方法扩展到多步和循环 LLM 应用。LLM-AutoDiff 构建了一个表示工作流的计算图，并利用**“反向引擎” LLM** 生成反馈，指导跨功能节点和重复调用的迭代提示优化。该框架还引入了选择性梯度计算和双阶段验证等技术，以提高优化效率。实验结果表明，LLM-AutoDiff 在多个任务上的准确性和训练成本方面均优于现有方法，为 LLM 部署的自动化和规模化提供了一种新范式。
原文链接：https://arxiv.org/abs/2501.16673
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By 任雨山Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：LLM-AutoDiff: Auto-Differentiate Any LLM WorkflowSummary
The provided research introduces LLM-AutoDiff, a novel framework for automating prompt engineering for complex Large Language Model workflows. This system extends gradient-based optimization to multi-step and cyclic LLM applications by treating textual inputs as trainable parameters. LLM-AutoDiff constructs a graph representing the workflow, enabling a "backward engine" LLM to generate feedback that guides iterative prompt improvements, even across functional nodes and repeated calls. The framework incorporates techniques like selective gradient computation and two-stage validation to enhance efficiency. Experimental results demonstrate that LLM-AutoDiff outperforms existing methods in accuracy and training cost across various tasks, offering a new paradigm for scaling and automating LLM deployments.
该研究提出了 LLM-AutoDiff，一个自动化提示工程（prompt engineering）的新框架，旨在优化复杂的大型语言模型（LLM）工作流。该系统通过将文本输入视为可训练参数，将基于梯度的优化方法扩展到多步和循环 LLM 应用。LLM-AutoDiff 构建了一个表示工作流的计算图，并利用**“反向引擎” LLM** 生成反馈，指导跨功能节点和重复调用的迭代提示优化。该框架还引入了选择性梯度计算和双阶段验证等技术，以提高优化效率。实验结果表明，LLM-AutoDiff 在多个任务上的准确性和训练成本方面均优于现有方法，为 LLM 部署的自动化和规模化提供了一种新范式。
原文链接：https://arxiv.org/abs/2501.16673