24 citations · 32 across the 3 of their papers we have counts for
3 papers
cs.NE2024★ 7 cited
Explaining Genetic Programming Trees using Large Language Models
Paula Maddigan, Andrew Lensen, Bing Xue
Genetic programming (GP) has the potential to generate explainable results, especially when used for dimensionality reduction. In this research, we investigate the potential of lev…
cs.HC2023★ 1 cited
Chat2VIS: Fine-Tuning Data Visualisations using Multilingual Natural Language Text and Pre-Trained Large Language Models
Paula Maddigan, Teo Susnjak
The explosion of data in recent years is driving individuals to leverage technology to generate insights. Traditional tools bring heavy learning overheads and the requirement for u…
cs.HC2023★ 24 cited
Chat2VIS: Generating Data Visualisations via Natural Language using ChatGPT, Codex and GPT-3 Large Language Models
Paula Maddigan, Teo Susnjak
The field of data visualisation has long aimed to devise solutions for generating visualisations directly from natural language text. Research in Natural Language Interfaces (NLIs)…