AutoFlow: Automated Workflow Generation for Large Language Model Agents
Project Overview
The document introduces AutoFlow, an innovative framework that automates the generation of workflows for AI agents utilizing large language models (LLMs), effectively addressing the complexities and time-consuming nature of manual workflow design. AutoFlow employs two distinct methods for generating workflows: a fine-tuning approach tailored for open-source LLMs and an in-context learning strategy suited for closed-source LLMs. Experimental findings demonstrate that workflows produced by AutoFlow significantly surpass those crafted manually in terms of task performance, while also minimizing the human effort required for their creation. This advancement showcases the potential of generative AI in enhancing educational processes by streamlining operational workflows, ultimately leading to more efficient and effective learning environments. The overall implications of AutoFlow suggest a transformative impact on how AI can be harnessed in education, promoting improved outcomes through automation and innovation.
Key Applications
AutoFlow framework for automated workflow generation
Context: Used for generating workflows that guide LLMs in complex task solving, targeting AI researchers and developers.
Implementation: The framework employs a workflow generator LLM that takes user queries and generates workflows in natural language. It utilizes reinforcement learning to optimize the workflow based on performance feedback.
Outcomes: Generated workflows show improved performance compared to manually created workflows and are easier to interpret.
Challenges: Dependency on the quality of the training data and potential inefficiencies in the learning process.
Implementation Barriers
Technical Barrier
The manual crafting of workflows is time-consuming and requires deep domain knowledge.
Proposed Solutions: AutoFlow aims to automate workflow generation, significantly reducing the need for manual intervention.
Performance Barrier
LLMs may generate non-executable or invalid workflows.
Proposed Solutions: The AutoFlow framework incorporates a reinforcement learning mechanism to refine workflow generation based on execution performance.
Project Team
Zelong Li
Researcher
Shuyuan Xu
Researcher
Kai Mei
Researcher
Wenyue Hua
Researcher
Balaji Rama
Researcher
Om Raheja
Researcher
Hao Wang
Researcher
He Zhu
Researcher
Yongfeng Zhang
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Zelong Li, Shuyuan Xu, Kai Mei, Wenyue Hua, Balaji Rama, Om Raheja, Hao Wang, He Zhu, Yongfeng Zhang
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai