Leveraging Large Language Models to Build and Execute Computational Workflows

Alejandro Duque, Abdullah Syed, Kastan V. Day, Matthew J. Berry, Daniel S. Katz +1 more
12/12/2023
cs.AI

No confident code implementation found for this paper. 3 possible matches can be shown below.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 3 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Natural Language Commanding via Program Synthesis

85% match2023

Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow

85% match1 repo1.5k2023

A Modular Reference Architecture for MCP-Servers Enabling Agentic BIM Interaction

85% match2025

Action Engine: Automatic Workflow Generation in FaaS

85% match2024

Parrot: Efficient Serving of LLM-based Applications with Semantic Variable

84% match1 repo2232024

ChainBuddy: An AI Agent System for Generating LLM Pipelines

84% match2024

Cite this paper

@article{duque2023leveraging,
  title  = {Leveraging Large Language Models to Build and Execute Computational Workflows},
  author = {Alejandro Duque and Abdullah Syed and Kastan V. Day and Matthew J. Berry and Daniel S. Katz and Volodymyr V. Kindratenko},
  year   = {2023},
  eprint = {2312.07711},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.07711v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2312.07711.svg)](https://codeofpaper.com/paper/2312.07711)

Discussion