Fuzzy logic in large language models: Concepts, applications, and challenges

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation; however, they continue to suffer from critical limitations, including predictive uncertainty, factual hallucination, and lack of interpretability. Fuzzy logic, with its inherent capacity to model graded membership and reason under imprecision, has emerged as a promising complementary paradigm to mitigate these shortcomings. Despite the rapidly growing body of literature, a systematic review that comprehensively synthesizes integration methodologies, empirical gains, and deployment challenges in fuzzy-LLM systems remains absent. This study conducts a systematic literature review of ۷۰ peer-reviewed studies published between November ۲۰۲۲ and June ۲۰۲۶, organized around five research questions: (۱) theoretical frameworks for fuzzy-LLM integration, (۲) improvements in reasoning performance, (۳)  allucination mitigation mechanisms, (۴) fuzzy evaluation metrics, and (۵) practical implementation obstacles. Our analysis reveals that fuzzy rule-based systems dominate the integration landscape, comprising ۴۰ studies (۵۷.۱%), whereas fuzzy attention mechanisms remain critically underexplored, with only ۳ studies (۴.۳%). Uncertainty reasoning constitutes the predominant application domain, addressed in ۵۴ studies (۷۷.۱%), while causal reasoning remains entirely uninvestigated. Empirical findings indicate that fuzzy mechanisms yield measurable accuracy improvements between ۱.۵% and ۳.۰%, alongside substantial hallucination reduction. Among mitigation strategies, fuzzy refinement emerges as both the most  frequently adopted and effective approach, reported in ۱۹ studies (۲۷.۱%). Nevertheless, large-scale deployment continues to face persistent barriers, particularly computational overhead and rule explosion, partially mitigated through  Type-۲ fuzzy systems and differentiable fuzzy layers. This review identifies fuzzy attention, standardized benchmarks, and  causal reasoning as pivotal directions for future inquiry, offering a foundational reference for researchers and  practitioners developing reliable, interpretable, and uncertainty-aware LLM-based systems.

کلیدواژه ها:

Fuzzy logic ، large language models ، hallucination reduction ، reasoning under uncertainty ، interpretability