@inproceedings{corallo-etal-2025-tablekv,
    title = "{T}able{KV}: {KV} Cache Compression for In-Context Table Processing",
    author = "Corallo, Giulio  and
      Faure-Rolland, Elia  and
      Lamari, Miriam  and
      Papotti, Paolo",
    editor = "Chang, Shuaichen  and
      Hulsebos, Madelon  and
      Liu, Qian  and
      Chen, Wenhu  and
      Sun, Huan",
    booktitle = "Proceedings of the 4th Table Representation Learning Workshop",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.trl-workshop.13/",
    pages = "166--171",
    ISBN = "979-8-89176-268-8",
    abstract = "Processing large tables provided in-context to LLMs is challenging due to token limits and information overload. While Retrieval-Augmented Generation can select relevant subsets externally, this work explores Key-Value (KV) cache compression as an alternative, applied directly to the linearized table during inference. We show that the LLM{'}s internal attention scores over the table context guides the retention of essential KV pairs, effectively compressing the processing context while preserving crucial relational information needed for complex queries. Experiments on Spider, WikitableQA, and QTSumm datasets validate the compression approach for in-context table processing, offering a promising path for improved table representation learning in LLMs."
}