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.

本文探讨了在大规模多目标优化领域中，生成 AI（GenAI）和进化算法（EAs）的无缝集成。通过着眼于大型语言模型（LLMs）的变革性作用，我们的研究调查了 LLM 辅助推理在自动化和增强决策过程中的潜力。具体而言，我们突出了 LLM 在揭示进化优化解决方案中的关键决策变量和表述上下文权衡方面的有效性。我们的方法旨在解决在大规模推断复杂多目标优化解决方案中固有的挑战，强调 LLM 的适应性，使其能够提供细致入微的解释，并使其语言与不同利益相关者的专业水平和领域偏好保持一致。经验证明，LLM 辅助推理在实际决策场景中具有实际可应用性和影响力。