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Enhancing Decision-Making in Optimization through LLM-Assisted Inference: A Neural Networks Perspective

Gaurav Singh, Kavitesh Kumar Bali

TL;DR

The paper tackles the challenge of decision support in large-scale multi-objective optimization by integrating GenAI with Evolutionary Algorithms through LLM-Assisted Inference. It demonstrates how LLMs can filter key decision variables, automate Pareto-front inference at scale, and tailor explanations to different levels of domain expertise and goals, all within a sustainable infrastructure planning case using NSGA-II. The main contributions include a structured LLM-based workflow for variable importance, Pareto-front interpretation, and stakeholder-aware narrative generation, supported by a GPT-3.5-based implementation. The results suggest improved transparency, more nuanced trade-off understanding, and scalable decision-making capabilities that can enhance real-world planning and policy discussions.

Abstract

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.

Enhancing Decision-Making in Optimization through LLM-Assisted Inference: A Neural Networks Perspective

TL;DR

The paper tackles the challenge of decision support in large-scale multi-objective optimization by integrating GenAI with Evolutionary Algorithms through LLM-Assisted Inference. It demonstrates how LLMs can filter key decision variables, automate Pareto-front inference at scale, and tailor explanations to different levels of domain expertise and goals, all within a sustainable infrastructure planning case using NSGA-II. The main contributions include a structured LLM-based workflow for variable importance, Pareto-front interpretation, and stakeholder-aware narrative generation, supported by a GPT-3.5-based implementation. The results suggest improved transparency, more nuanced trade-off understanding, and scalable decision-making capabilities that can enhance real-world planning and policy discussions.

Abstract

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.
Paper Structure (27 sections, 3 figures, 3 tables)

This paper contains 27 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Large Language Models
  3. Evolution and Development
  4. Applications
  5. Evolutionary Multi-objective Optimization: A brief review
  6. Basic Formulations and Definitions:
  7. Explainable AI
  8. Brief Review of Explainable AI
  9. Connecting LLMs for Explainability
  10. LLM-Assisted inference: A case study
  11. Case study overview: Sustainable Infrastructure Planning via EMO
  12. Optimization Problem
  13. EMO-Driven Optimization Approach
  14. Standard Approach to Solution Inferencing: Manual Inspection and Statistical Analysis
  15. LLM-Assisted Inference
  16. ...and 12 more sections

Figures (3)

  • Figure 1: Evolution: AI$\rightarrow$ ML$\rightarrow$ GenAI WinNT
  • Figure 2: Visualization of a 2-objective Pareto front for a minimization problem generated by an evolutionary algorithm. Solutions at the extremes prioritize a single objective, while those in the middle characterize knee solutions, achieving a balanced compromise between conflicting objectives heidari2022finding.
  • Figure 3: Pareto Front Illustrating the Trade-off Between Total Cost (M$) and Environmental Impact