# Explaining Genetic Programming Trees using Large Language Models

arXiv: https://arxiv.org/abs/2403.03397 · HTML: https://mlsanity.com/paper/2403.03397/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2024.

## Built on by (most cited first, 6 of 6)

- [A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving](https://mlsanity.com/paper/2509.08269/) (2025)
- [Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms](https://mlsanity.com/paper/2503.10968/) (2025)
- [LLMs as Debate Partners: Utilizing Genetic Algorithms and Adversarial Search for Adaptive Arguments](https://mlsanity.com/paper/2412.06229/) (2024)
- [EvoSpeak: Large Language Models for Interpretable Genetic Programming-Evolved Heuristics](https://mlsanity.com/paper/2510.02686/) (2025)
- [Genetic Programming for Explainable Manifold Learning](https://mlsanity.com/paper/2403.14139/) (2024)
- [Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap](https://mlsanity.com/paper/2505.19625/) (2025)

## Abstract (arXiv)

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.