# Leveraging Large Language Models to Build and Execute Computational Workflows

arXiv: https://arxiv.org/abs/2312.07711 · HTML: https://mlsanity.com/paper/2312.07711/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Builds on (most cited first, 2 of 2)

- [Parsl: Pervasive Parallel Programming in Python](https://mlsanity.com/paper/1905.02158/) (2019)
- [A Comparative Study of Code Generation using ChatGPT 3.5 across 10 Programming Languages](https://mlsanity.com/paper/2308.04477/) (2023)

## Built on by (most cited first, 1 of 1)

- [Training Next Generation AI Users and Developers at NCSA](https://mlsanity.com/paper/2406.14744/) (2024)

## Abstract (arXiv)

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.