  This paper explores the seamless integration of Generative AI (GenAI) and
Evolutionary Algorithms (EAs) within the domain of large-scale multi-objective
optimization. Focusing on the transformative role of Large Language Models
(LLMs), our study investigates the potential of LLM-Assisted Inference to
automate and enhance decision-making processes. Specifically, we highlight its
effectiveness in illuminating key decision variables in evolutionarily
optimized solutions while articulating contextual trade-offs. Tailored to
address the challenges inherent in inferring complex multi-objective
optimization solutions at scale, our approach emphasizes the adaptive nature of
LLMs, allowing them to provide nuanced explanations and align their language
with diverse stakeholder expertise levels and domain preferences. Empirical
studies underscore the practical applicability and impact of LLM-Assisted
Inference in real-world decision-making scenarios.
