  Genetic programming (GP) has the potential to generate explainable results,
especially when used for dimensionality reduction. In this research, we
investigate the potential of leveraging eXplainable AI (XAI) and large language
models (LLMs) like ChatGPT to improve the interpretability of GP-based
non-linear dimensionality reduction. Our study introduces a novel XAI dashboard
named GP4NLDR, the first approach to combine state-of-the-art GP with an
LLM-powered chatbot to provide comprehensive, user-centred explanations. We
showcase the system's ability to provide intuitive and insightful narratives on
high-dimensional data reduction processes through case studies. Our study
highlights the importance of prompt engineering in eliciting accurate and
pertinent responses from LLMs. We also address important considerations around
data privacy, hallucinatory outputs, and the rapid advancements in generative
AI. Our findings demonstrate its potential in advancing the explainability of
GP algorithms. This opens the door for future research into explaining GP
models with LLMs.
