@inproceedings{wang-etal-2026-task,
    title = "Task-Related In-Context Learning",
    author = "Wang, Wenqiang  and
      Chen, Peng  and
      Xiao, Yan  and
      Zhang, Yangshijie  and
      Lu, Xiaoyue  and
      Huang, Jianjie  and
      Cao, Xiaochun",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.2154/",
    pages = "43378--43400",
    ISBN = "979-8-89176-395-1",
    abstract = "Standard in-context learning (ICL) assumes identical output spaces between test and retrieval datasets (fully aligned). However, in practice, these datasets can be fully aligned, partially aligned, or fully disjoint in label space (Output space), forming an information continuum from rich to scarce. Naive ICL often becomes ineffective under such mismatches. In this work, we challenge this assumption by demonstrating that the retrieval dataset need not perfectly align with the test dataset, as long as it remains related to the target task. We propose Task-Related In-Context Learning (TRICL), a unified framework for ICL under output-space mismatch, designed to cover the full continuum of scenarios. TRICL first identifies demonstrations in the mismatched retrieval dataset that are relevant to the test label space via a lightweight Bayesian probabilistic criterion, and uses them to form a related dataset. TRICL then perform ICL on the related dataset to obtain preliminary predictions; finally, TRICL leverage these intermediate predictions to reduce and transform the output space of the original test task, thereby improving the performance of LLMs. Even in the most information-scarce fully disjoint scenario, as long as the retrieval dataset is task-related to the test task, TRICL achieves state-of-the-art (SOTA) results across three LLMs, three task types, and four datasets. Moreover, TRICL remains effective in the fully aligned and partially aligned scenarios, consistently yielding strong gains over competitive baselines. Moreover, TRICL also extends to generative task."
}