Hemolix.TabGen: Optimized Table Generation from Documents

Gyanendra Shrestha, Todor Ivanov, Karthik Vemireddy, Anna Pyayt, Michael Gubanov

Keywords: Data Management, Artificial Intelligence, Large Language Model (LLM)

Abstract:

Modern Data Lakes contain vast and heterogeneous document collections, making table generation from documents a persistent and nontrivial challenge. Traditional approaches are often rigid — i.e. domain-specific, require extensive supervision, or are limited to set of pre-defined schemas; LLM-based approaches are more flexible, but typically suffer from hallucinations, non-determinism, and high computational costs. To overcome these limitations, we introduce Hemolix.TabGen, a novel scalable LLM-based table generation system
that comprehends documents and generates Bi-dimensional tables based on the entire document content. We evaluated TabGen on 4 publicly available datasets spanning multiple domains and observed an Average Precision delta up to 30% compared to vanilla LLMs

Submission Type: Emerging

Submission Number: 240