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English(EN) Generating Workflow DAGs from Natural Language with Non-Reasoning LLMs

新方法使用LLM从自然语言生成复杂工作流DAG

一篇新的研究论文提出了一种神经符号方法,可以根据自然语言指令生成复杂的工作流定向无环图(DAG)。该方法通过将组合图构建分离到一个确定性编译器中,利用了成本较低的非推理大型语言模型。该系统在生成有效的JSON工作流方面实现了高精度,并且在GPT-5.3-chat等模型上比整体提示方法有了显著改进。 AI

影响 通过使非专业用户能够通过自然语言定义复杂的自动化工作流,这种方法可以简化企业环境中复杂自动化工作流的创建。

排序理由 该集群包含一篇详细介绍从自然语言生成结构化数据的新颖方法的 ist.

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新方法使用LLM从自然语言生成复杂工作流DAG

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该集群包含一篇详细介绍从自然语言生成结构化数据的新颖方法的 ist.
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Anand Iyer, Bhanu Khetharpal, Srinivas Upadhya, Ramkumar Rajagopal ·

    使用非推理LLM从自然语言生成工作流DAG

    arXiv:2608.30250v1 Announce Type: new Abstract: This paper addresses the problem of translating natural-language routing rules written by business administrators into executable workflow graphs for enterprise contact centers. Each target is a directed acyclic graph (DAG) of condi…