Fuzzy Reasoning Chain (FRC): An Innovative Reasoning Framework from Fuzziness to Clarity

2025-09-26

AI Summary

To address the pervasive challenges of ambiguity, polysemy, and uncertainty modeling in textual data, this paper proposes the Fuzzy Reasoning Chain (FRC) framework. FRC innovatively integrates large language models’ semantic priors, continuous fuzzy membership functions, and a probabilistic–fuzzy hybrid inference mechanism to explicitly model and jointly resolve uncertainty and conflicting signals. Its progressive “fuzzy-to-crisp” reasoning architecture enhances decision interpretability and facilitates cross-scale knowledge transfer. Empirical evaluation on sentiment analysis demonstrates that FRC achieves robust performance significantly surpassing baseline methods while maintaining stable inference accuracy. Theoretical analysis further establishes the consistency and convergence guarantees of FRC’s inference process. Collectively, FRC advances uncertainty-aware natural language understanding by unifying symbolic fuzzy logic with neural semantic representations in a principled, interpretable, and scalable manner.

Technology Category

• Reasoning under Uncertainty: Uncertainty Representations
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Knowledge Representation and Reasoning: Common-Sense Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

With the rapid advancement of large language models (LLMs), natural language processing (NLP) has achieved remarkable progress. Nonetheless, significant challenges remain in handling texts with ambiguity, polysemy, or uncertainty. We introduce the Fuzzy Reasoning Chain (FRC) framework, which integrates LLM semantic priors with continuous fuzzy membership degrees, creating an explicit interaction between probability-based reasoning and fuzzy membership reasoning. This transition allows ambiguous inputs to be gradually transformed into clear and interpretable decisions while capturing conflicting or uncertain signals that traditional probability-based methods cannot. We validate FRC on sentiment analysis tasks, where both theoretical analysis and empirical results show that it ensures stable reasoning and facilitates knowledge transfer across different model scales. These findings indicate that FRC provides a general mechanism for managing subtle and ambiguous expressions with improved interpretability and robustness.

Problem

• Handles ambiguous and uncertain text inputs
• Transforms fuzzy information into clear decisions
• Improves interpretability in sentiment analysis tasks

Innovation

• Integrates LLM semantic priors with fuzzy membership degrees
• Transforms ambiguous inputs into clear interpretable decisions
• Provides improved interpretability and robustness for ambiguity