Year: 2025
Type: conference-paper
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, Wik-itableQA, and QTSumm datasets validate the compression approach for in-context table processing, offering a promising path for improved table representation learning in LLMs.
Authors: Giulio Corallo, Elia Faure-Rolland, Miriam Lamari, Paolo Papotti
Institution: EURECOM
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412944083