Benchmarking Agentic Workflow Generation
Authors: name: Shuofei Qiao, name: Runnan Fang, name: Zhisong Qiu, name: Xiaobin Wang, name: Ningyu Zhang, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Huajun Chen

Large Language Models (LLMs), with their exceptional ability to handle a wide
range of tasks, have driven significant advancements in tackling reasoning and
planning tasks, wherein decomposing complex problems into executable workflows
is a crucial step in this process. Existing workflow evaluation frameworks
either focus solely on holistic performance or suffer from limitations such as
restricted scenario coverage, simplistic workflow structures, and lax
evaluation standards. To this end, we introduce WorFBench, a unified workflow
generation benchmark with multi-faceted scenarios and intricate graph workflow
structures. Additionally, we present WorFEval, a systemic evaluation protocol
utilizing subsequence and subgraph matching algorithms to accurately quantify
the LLM agent's workflow generation capabilities. Through comprehensive
evaluations across different types of LLMs, we discover distinct gaps between
the sequence planning capabilities and graph planning capabilities of LLM
agents, with even GPT-4 exhibiting a gap of around 15%. We also train two
open-source models and evaluate their generalization abilities on held-out
tasks. Furthermore, we observe that the generated workflows can enhance
downstream tasks, enabling them to achieve superior performance with less time
during inference. Code and dataset will be available at
https://github.com/zjunlp/WorFBench.