Fuzzy Reasoning Chain (FRC): An Innovative Reasoning Framework from Fuzziness to Clarity
Ping Chen, Xiang Liu, Zhaoxiang Liu, Zezhou Chen, Xingpeng Zhang, Huan Hu, Zipeng Wang, Kai Wang
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.
BibTeX
@inproceedings{emnlp2025_fuzzyreasoningch,
title = {Fuzzy Reasoning Chain (FRC): An Innovative Reasoning Framework from Fuzziness to Clarity},
author = {Ping Chen and Xiang Liu and Zhaoxiang Liu and Zezhou Chen and Xingpeng Zhang and Huan Hu and Zipeng Wang and Kai Wang and Shuming Shi and Shiguo Lian},
booktitle = {EMNLP 2025},
year = {2025}
}