@inproceedings{oh-lee-2026-late,
    title = "Late Code Chunking: A Code Chunking Strategy for Repository-Level Code Completion",
    author = "Oh, Seungmin  and
      Lee, Eunseok",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-short.64/",
    pages = "780--786",
    ISBN = "979-8-89176-391-3",
    abstract = "This paper introduces Late Code Chunking (LC$^2$), a chunking strategy designed to improve the semantic understanding of code segments for Large Language Models (LLMs). Repository-level code completion requires predicting the completion of unfinished code by leveraging cross-file context spread across a repository. However, when retrieved fragments have missing semantics{---}the loss of structural or behavioral information during chunking{---}LLMs struggle to interpret the target code. To address this, LC$^2$ refines retrieved chunks by constructing a dual context: a ``Code Retrieval Context'' optimized for similarity-based search, and a ``Code Comprehension Context'' that serves as a late enrichment step through context expansion and augmentation. This dual-context design reduces information loss due to chunking and enhances the ability of LLMs to utilize retrieved code. Additionally, we introduce an Asymmetric Query-Chunk Sizing strategy to further optimize retrieval quality by minimizing query noise. Our experiments demonstrate that LC$^2$ provides robust performance gains, achieving a statistically significant 19.7{\%} improvement in Exact Match accuracy on the CrossCodeEval benchmark compared to the best existing chunking method."
}