@inproceedings{gao-etal-2025-icler,
    title = "{ICLER}: Intent {CL}assification with Enhanced Reasoning",
    author = "Gao, Dezheng  and
      Xiaozheng, Dong  and
      Yang, SHuangtao  and
      Fu, Bo",
    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/author-page-yu-wang-polytechnic/2025.findings-emnlp.164/",
    doi = "10.18653/v1/2025.findings-emnlp.164",
    pages = "3031--3044",
    ISBN = "979-8-89176-335-7",
    abstract = "In recent years, intent classification technology based on In-Context Learning (ICL) has made significant progress. However, when applied to enterprise vertical domains, existing methods are inadequate in identifying micro-grained intentions. This study identifies two primary causes of errors in data analysis: (1) Retrieving incorrect instances, this is often due to the limitations of embedding models in capturing subtle sentence-level information in business scenarios (such as entity-related or phenomenon-specific details) (2) Insufficient reasoning ability of Large Language Models (LLMs), which tend to rely on surface-level semantics while overlooking deeper semantic associations and business logic, leading to misclassification. To address these issues, we propose ICLER, an intent classification method with enhanced reasoning. This method first optimizes the embedding model by introducing a reasoning mechanism to enhance its ability to fine-grained sentence-level information. Then, this mechanism is incorporated into the ICL framework, maintaining computational efficiency while significantly enhancing intent recognition accuracy. Experimental results demonstrate that ICLER significantly outperforms the original ICL method in intent identification within vertical domains. Moreover, it yields accuracy improvements of 0.04{\%} to 1.14{\%} on general datasets and its fine-tuned embedding model achieves an average performance gain of 5.56{\%} on selected classification tasks in the MTEB benchmark."
}