Summary of Benchmarking Agentic Workflow Generation, by Shuofei Qiao et al.
Benchmarking Agentic Workflow Generation
by Shuofei Qiao, Runnan Fang, Zhisong Qiu, Xiaobin Wang, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have revolutionized the realm of reasoning and planning tasks by efficiently breaking down complex problems into executable workflows. However, existing workflow evaluation frameworks are limited in their scope, often focusing solely on holistic performance or neglecting crucial aspects like scenario coverage, workflow structure complexity, and rigorous evaluation standards. To address these shortcomings, this paper introduces WorfBench, a unified benchmark for workflow generation that encompasses multi-faceted scenarios and intricate graph structures. Additionally, the authors propose WorfEval, a systemic protocol utilizing subsequence and subgraph matching algorithms to quantify LLM agents’ workflow generation capabilities. The study reveals distinct gaps between sequence planning and graph planning abilities across different LLM types, including GPT-4, which exhibits a 15% gap. The researchers also train open-source models and evaluate their generalization capabilities on held-out tasks. Notably, the generated workflows can enhance downstream tasks, enabling them to achieve superior performance with reduced inference time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are very smart computers that can do many things, like solve problems and make plans. But when it comes to breaking down big problems into smaller steps, there are some limitations in how we test their abilities. This paper wants to fix this by creating a new way to evaluate LLMs’ planning skills called WorfBench. It’s like a puzzle that tests the computer’s ability to make good plans. The authors also came up with a new method to measure how well LLMs do at planning, which is important because it shows us what they can and can’t do. They found out that some LLMs are really good at one type of planning but not as good at another. This could help us make better use of these computers in the future. |
Keywords
» Artificial intelligence » Generalization » Gpt » Inference
