Fuzzy Reasoning Chain (FRC): An Innovative Reasoning Framework from Fuzziness to Clarity
This addresses the challenge of managing subtle and ambiguous expressions in NLP for improved model interpretability and robustness, though it appears incremental as it builds on existing LLM and fuzzy logic methods.
The paper tackles the problem of handling ambiguous, polysemous, or uncertain texts in NLP by introducing the Fuzzy Reasoning Chain (FRC) framework, which integrates LLM semantic priors with fuzzy membership degrees to transform ambiguous inputs into clear decisions, validated on sentiment analysis tasks with improved interpretability and robustness.
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.