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.

翻译：遗传编程（GP）具有生成可解释结果的潜力，尤其是在降维应用中。本研究探讨了利用可解释人工智能（XAI）和大语言模型（如ChatGPT）提升基于GP的非线性降维方法可解释性的可能性。我们提出一种名为GP4NLDR的新型XAI仪表盘，这是首个将前沿GP技术与大语言模型驱动的聊天机器人相结合的方法，旨在提供全面且以用户为中心的解释。通过案例研究，我们展示了该系统为高维数据降维过程提供直观且富有洞察力的叙述性解释的能力。研究强调了提示工程在引导大语言模型生成准确且相关回应中的重要性，同时探讨了数据隐私、幻觉输出以及生成式人工智能快速发展等关键问题。研究结果表明该方法在提升GP算法可解释性方面具有潜力，为后续利用大语言模型解释GP模型的研究开辟了新方向。