Finetuning LLMs for Automatic Concept to TTI Prompt Generation (Student Abstract)

Authors

  • Jeremy Rutter University of South Florida
  • Maneesh Reddy Chamakura University of South Florida
  • Justin Delgado University of South Florida
  • Gene Louis Kim University of South Florida

DOI:

https://doi.org/10.1609/aaai.v38i21.30505

Keywords:

Applications of AI, Statistical Learning, Machine Learning, Text Generation

Abstract

Our work explores bridging the gap between large language models and text-to-image models to create a tool for quickly and easily generating high quality images from a given concept. In our experiments we successfully improved image quality with only a preliminary utilization of the available resources for finetuning.
AAAI-24 / IAAI-24 / EAAI-24 Proceedings Cover

Downloads

Published

2024-03-24

How to Cite

Rutter, J., Chamakura, M. R., Delgado, J., & Kim, G. L. (2024). Finetuning LLMs for Automatic Concept to TTI Prompt Generation (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23637–23639. https://doi.org/10.1609/aaai.v38i21.30505

Issue

Vol. 38 No. 21: IAAI-24, EAAI-24, AAAI-24 Student Abstracts, Undergraduate Consortium and Demonstrations

Section

AAAI Student Abstract and Poster Program