@inproceedings{zhang-etal-2025-va,
    title = "{D}.{V}a: Validate Your Demonstration First Before You Use It",
    author = "Zhang, Qi  and
      Xiao, Zhiqing  and
      Xiao, Ruixuan  and
      Gao, Lirong  and
      Zhao, Junbo",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.129/",
    pages = "2580--2594",
    ISBN = "979-8-89176-251-0",
    abstract = "In-context learning (ICL) has demonstrated significant potential in enhancing the capabilities of large language models (LLMs) during inference. It{'}s well-established that ICL heavily relies on selecting effective demonstrations to achieve outputs that better align with the expected results. As for demonstration selection, previous approaches have typically relied on intuitive metrics to evaluate the effectiveness of demonstrations, which often results in limited robustness and poor cross-model generalization capabilities. To tackle these challenges, we propose a novel method, **D**emonstration **Va**lidation (**D.Va**), which integrates a demonstration validation perspective into this field. By introducing the demonstration validation mechanism, our method effectively identifies demonstrations that are both effective and highly generalizable. **D.Va** surpasses all existing retrieval-based in-context learning techniques across both natural language understanding (NLU) and natural language generation (NLG) tasks. Additionally, we demonstrate the robustness and generalizability of our approach across various language models and retrieval models."
}