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New Benchmark Tests LLMs' Ability to Generate Visual Workflows

Researchers have introduced Chat2Workflow, a new benchmark designed to evaluate the ability of large language models (LLMs) to generate executable visual workflows from natural language prompts. The benchmark, derived from real-world business workflows, aims to automate the current manual process of workflow construction, which is often costly and error-prone. While current LLMs can grasp high-level intentions, they struggle with generating accurate and deployable workflows, with even an advanced agentic baseline achieving only a 6.05% improvement in resolution rate, highlighting the need for further advancements in industrial-grade automation. AI

IMPACT This benchmark could drive progress in automating complex task execution via LLMs, potentially streamlining business process development.

RANK_REASON The cluster describes a new academic benchmark for evaluating LLM capabilities in generating visual workflows. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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New Benchmark Tests LLMs' Ability to Generate Visual Workflows

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yi Zhong, Buqiang Xu, Yijun Wang, Zifei Shan, Shuofei Qiao, Guozhou Zheng, Ningyu Zhang ·

    Chat2Workflow: A Benchmark for Generating Executable Visual Workflows with Natural Language

    arXiv:2604.19667v2 Announce Type: replace-cross Abstract: At present, executable visual workflows have emerged as a mainstream paradigm in real-world industrial deployments, offering strong reliability and controllability. However, in current practice, such workflows are almost e…