@inproceedings{chen-etal-2025-fuzzy,
    title = "Fuzzy Reasoning Chain ({FRC}): An Innovative Reasoning Framework from Fuzziness to Clarity",
    author = "Chen, Ping  and
      Liu, Xiang  and
      Liu, Zhaoxiang  and
      Chen, Zezhou  and
      Zhang, Xingpeng  and
      Hu, Huan  and
      Wang, Zipeng  and
      Wang, Kai  and
      Shi, Shuming  and
      Lian, Shiguo",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-luhme/2025.findings-emnlp.541/",
    doi = "10.18653/v1/2025.findings-emnlp.541",
    pages = "10230--10240",
    ISBN = "979-8-89176-335-7",
    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."
}