@inproceedings{kweon-etal-2025-topic,
    title = "Topic Coverage-based Demonstration Retrieval for In-Context Learning",
    author = "Kweon, Wonbin  and
      Kang, SeongKu  and
      Tian, Runchu  and
      Jiang, Pengcheng  and
      Han, Jiawei  and
      Yu, Hwanjo",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1007/",
    pages = "19911--19923",
    ISBN = "979-8-89176-332-6",
    abstract = "The effectiveness of in-context learning relies heavily on selecting demonstrations that provide all the necessary information for a given test input.To achieve this, it is crucial to identify and cover fine-grained knowledge requirements. However, prior methods often retrieve demonstrations based solely on embedding similarity or generation probability, resulting in irrelevant or redundant examples.In this paper, we propose TopicK, a topic coverage-based retrieval framework that selects demonstrations to comprehensively cover topic-level knowledge relevant to both the test input and the model.Specifically, TopicK estimates the topics required by the input and assesses the model{'}s knowledge on those topics.TopicK then iteratively selects demonstrations that introduce previously uncovered required topics, in which the model exhibits low topical knowledge.We validate the effectiveness of TopicK through extensive experiments across various datasets and both open- and closed-source LLMs.Our source code is available at https://github.com/WonbinKweon/TopicK{\_}EMNLP2025."
}