Enhancing Decision-Making in Optimization through LLM-Assisted Inference: A Neural Networks Perspective
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909200467099648 |
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| author | Singh, Gaurav Bali, Kavitesh Kumar |
| author_facet | Singh, Gaurav Bali, Kavitesh Kumar |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_07212 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Decision-Making in Optimization through LLM-Assisted Inference: A Neural Networks Perspective Singh, Gaurav Bali, Kavitesh Kumar Neural and Evolutionary Computing Artificial Intelligence 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. |
| title | Enhancing Decision-Making in Optimization through LLM-Assisted Inference: A Neural Networks Perspective |
| topic | Neural and Evolutionary Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2405.07212 |