Enhancing Large Language Models with Fuzzy Set Extensions: A Qualitative Exploration of Intuitionistic, Neutrosophic, and Plithogenic Frameworks

Abstract

Fuzzy set theory and its extensions - intuitionistic fuzzy sets (IFS), neutrosophic sets (NS), and plithogenic sets - provide robust frameworks for modeling uncertainty in complex systems. This research investigates their novel application in generative artificial intelligence (AI) and large language models (LLMs) to address challenges such as semantic ambiguity, contextual indeterminacy, and ethical decision-making. Employing a qualitative methodology, we analyze 12 peer-reviewed sources from Google Scholar to explore how these extensions enhance LLMs’ capabilities. We propose a unified framework integrating IFS, NS, and plithogenic sets to improve semantic accuracy, uncertainty handling, and multi-criteria decision-making. Findings from case studies across sentiment analysis, text summarization, ethical content generation, and diagnostics demonstrate the potential of these frameworks to create interpretable, adaptable, and ethically aligned LLMs. This study contributes significantly to the advancement of generative AI by offering a novel approach to uncertainty modeling, paving the way for more robust and responsible AI systems.