3 papers
cs.CL2026
ExStrucTiny: A Benchmark for Schema-Variable Structured Information Extraction from Document Images
Mathieu Sibue, Andres Muñoz Garza, Samuel Mensah +4
Enterprise documents, such as forms and reports, embed critical information for downstream applications like data archiving, automated workflows, and analytics. Although generalist…
cs.CL2026
Entropy-Gated Branching for Efficient Test-Time Reasoning
Xianzhi Li, Ethan Callanan, Abdellah Ghassel +1
Test-time compute methods can significantly improve the reasoning capabilities and problem-solving accuracy of large language models (LLMs). However, these approaches require subst…
cs.CL2024
"What is the value of {templates}?" Rethinking Document Information Extraction Datasets for LLMs
Ran Zmigrod, Pranav Shetty, Mathieu Sibue +4
The rise of large language models (LLMs) for visually rich document understanding (VRDU) has kindled a need for prompt-response, document-based datasets. As annotating new datasets…