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Chat2Workflow:生成自然语言可执行视觉工作流的基准
Yi Zhong, Bu · 2026-04-22 · via cs.LG updates on arXiv.org

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摘要:今 executable visual workflows 已成工业部署之主流范式,具强可靠与可控之效。然今之实践,此类工作流几全凭手工构建:开发者必精心设计流程,为每步撰拟指令,随需求变迁反复修正逻辑——致开发费时费力,且易生谬误。为究大语言模型能否自动化此多轮交互之过程,吾等创制 Chat2Workflow,此乃直接自自然语言生成 executable visual workflows 之基准,复提一稳健之 agentic baseline 以增其效。此基准乃自大量现实商业工作流集构建,每例皆设,俾所生工作流可转化并直接部署于 Dify、Coze 等实用工作流平台。实验结果显示,虽今之语言模型常能捕捉高层意图,然于生成正确、稳定、可执行之工作流,尤遇复杂变迁之要求时,则多感困顿。吾等之 agentic baseline 虽增解率高达 6.05%,然现实工业之鸿沟,仍使 Chat2Workflow 成推进工业级自动化之基石。代码存于此https URL
评论: 进行中
主题: 计算与语言 (cs.CL);人工智能 (cs.AI);计算机视觉与模式识别 (cs.CV);机器学习 (cs.LG);多智能体系统 (cs.MA)
引用方式: arXiv:2604.19667 [cs.CL]
  (或 arXiv:2604.19667v2 [cs.CL]为此版本)
  https://doi.org/10.48550/arXiv.2604.19667

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[v1] 周二,丙申年四月廿一,酉时四十九分十一秒,格林威治标准时间(二十九万二千七百三十三字节)
[v2] 周二,丙申年五月廿六,酉时十四分十秒,格林威治标准时间(九万八千零四字节)