AFlow: A New AI Framework for Automated Workflow Optimization

The challenge lies in generating effective agent workflows for large language models (LLM). Despite their notable capabilities across multiple tasks, creating workflows that combine multiple LLMs into coherent sequences is labor-intensive, limiting scalability and adaptability to new tasks. Efforts to automate workflow generation have not yet completely eliminated the need for human intervention, making it difficult to achieve broad generalization and effective skill transfer for LLMs.

A team of researchers from DeepWisdom, Hong Kong University of Science and technology (Guangzhou), Renmin University of China, Nanjing University, Fudan University, King Abdullah University of Science and technology, University of Montreal and Mila, Hong Kong University of Science and technology present AFlow, a novel framework aimed at automating the generation of agent workflows. AFlow is designed to solve existing challenges by framing the workflow optimization problem as a search for workflows represented by code. These workflows are modeled as graphs where nodes represent actions that invoke LLM and edges represent dependencies between these actions. Using Monte Carlo Tree Search (MCTS), AFlow iteratively optimizes workflows by making modifications, executing them, and refining the structure based on execution feedback.

The AFlow framework is designed to efficiently explore and optimize workflows with minimal human involvement. The key to AFlow's efficiency lies in its use of nodes and edges to represent workflows, allowing it to model complex relationships between LLM actions. The nodes are connected in a tree-like structure, allowing for various configurations to suit various task complexities. AFlow uses predefined operators, such as “Set” or “Review and Review,” which serve as modular building blocks. Workflow optimization progresses through a series of phases, including node exploration, expansion through LLM-based feedback, and experience backpropagation, ensuring AFlow can refine workflows with each iteration.

The results of this study, based on six benchmark datasets (HumanEval, MBPP, MATH, GSM8K, HotPotQA, and DROP), demonstrate that AFlow significantly outperforms manually designed state-of-the-art workflows as well as automated optimization approaches. existing. Specifically, AFlow achieves an average performance improvement of 5.7% over manually designed methods and a 19.5% improvement over existing automated systems such as ADAS. The researchers also noted that AFlow could generate workflows that allow smaller LLMs to outperform larger models like GPT-4o, all for just 4.55% of the inference cost, making it a cost-effective alternative. for a wide variety of tasks.

In conclusion, AFlow makes significant progress in reducing the need for manual effort in the design of agent workflows, thereby expanding the potential of LLMs to solve a wide range of tasks effectively. By using MCTS for search and workflow optimization, AFlow not only automates the process but also achieves better performance and cost-effectiveness compared to existing methods. This advancement provides a solid foundation for future research on automating workflow generation, making LLMs more accessible and efficient for real-world applications.

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