用模糊推理链处理语言歧义，让模型决策更清晰可信。
Fuzzy Reasoning Chain (FRC): An Innovative Reasoning Framework from Fuzziness to Clarity
- 将模糊隶属度与概率推理结合，动态转化模糊输入
- 在情感分析中提升推理稳定性与跨规模知识迁移能力
- 适合需要解释性和鲁棒性的复杂语义任务
随着大语言模型（LLMs）的快速发展，自然语言处理取得了显著进展。然而，在处理含歧义、多义或不确定性的文本时仍面临重大挑战。我们提出模糊推理链（FRC）框架，将LLM语义先验与连续模糊隶属度相结合，实现概率推理与模糊隶属推理的显式交互。该机制使模糊输入能逐步转化为清晰可解释的决策，同时捕捉传统概率方法无法处理的冲突或不确定性信号。我们在情感分析任务上验证了FRC，理论分析与实证结果均表明其能保证稳定推理，并促进不同模型规模间的知识迁移。这些发现表明，FRC为处理细微模糊表达提供了一种通用机制，具备更强的可解释性与鲁棒性。
原文摘要 · Abstract (English)
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.
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