Summary of Autoflow: Automated Workflow Generation For Large Language Model Agents, by Zelong Li et al.
AutoFlow: Automated Workflow Generation for Large Language Model Agents
by Zelong Li, Shuyuan Xu, Kai Mei, Wenyue Hua, Balaji Rama, Om Raheja, Hao Wang, He Zhu, Yongfeng Zhang
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper proposes a framework called AutoFlow, which automatically generates workflows for Large Language Model (LLM)-based AI Agents to solve complex tasks. The framework takes natural language programs as the format of agent workflow and employs a workflow optimization procedure to iteratively optimize the workflow quality. Two workflow generation methods are offered: fine-tuning-based and in-context-based, making AutoFlow applicable to both open-source and closed-source LLMs. Experimental results show that the framework can produce robust and reliable agent workflows. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way for AI agents to solve complex tasks. Usually, people have to design special procedures to help these agents do their jobs, but this takes a lot of time and knowledge. To make things easier, the researchers created a system called AutoFlow that can automatically generate these procedures. The system uses natural language programs and makes sure they’re good by testing them over and over. This could be very useful for making AI agents that can do lots of different tasks. |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Optimization
