@inproceedings{lin-etal-2025-cll,
    title = "{CLL}-{R}et{ICL}: Contrastive Linguistic Label Retrieval-based In-Context Learning for Text Classification via Large Language Models",
    author = "Lin, Chaohao  and
      Wu, Kaida  and
      Xiang, Peihao  and
      Wu, Yanzhao  and
      Bai, Ou",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.findings-ijcnlp.97/",
    pages = "1575--1590",
    ISBN = "979-8-89176-303-6",
    abstract = "Recent research has delved into Retrieval-based In-Context Learning (RetICL), leveraging the power of large language models (LLMs) for text classification. Despite its promise, a persistent challenge lies in effectively retrieving relevant demonstrations from a support set. Many existing approaches have overlooked the essential role of linguistic label information in guiding this retrieval process. To bridge this gap, we present Contrastive Linguistic Label Retrieval-based In-Context Learning (CLL-RetICL), a novel framework designed to identify the most relevant and impactful sentences without altering the model parameters. Our approach uniquely integrates sentence-query similarity with sentence-label similarity, enabling a more nuanced and comprehensive evaluation of relevance. We tested CLL-RetICL across diverse text classification tasks and evaluated its performance on various LLMs. Experimental results demonstrate that CLL-RetICL consistently outperforms previous retrieval methods that do not incorporate linguistic label information. These findings highlight the critical importance of linguistic label-aware selection in enhancing text classification accuracy."
}