Leveraging Large Language Models to Build and Execute Computational Workflows

Alejandro Duque; Abdullah Syed; Kastan V. Day; Matthew J. Berry; Daniel S. Katz; Volodymyr V. Kindratenko

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arXiv · 2312.07711

Leveraging Large Language Models to Build and Execute Computational Workflows

Alejandro Duque·Abdullah Syed·Kastan V. Day·Matthew J. Berry·Daniel S. Katz·Volodymyr V. Kindratenko

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Abstract

The recent development of large language models (LLMs) with multi-billion parameters, coupled with the creation of user-friendly application programming interfaces (APIs), has paved the way for automatically generating and executing code in response to straightforward human queries. This paper explores how these emerging capabilities can be harnessed to facilitate complex scientific workflows, eliminating the need for traditional coding methods. We present initial findings from our attempt to integrate Phyloflow with OpenAI's function-calling API, and outline a strategy for developing a comprehensive workflow management system based on these concepts.

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Alejandro Duque, Abdullah Syed, Kastan V. Day, Matthew J. Berry, Daniel S. Katz, Volodymyr V. Kindratenko. 2023-12-12. Leveraging Large Language Models to Build and Execute Computational Workflows. https://arxiv.org/abs/2312.07711

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