17 citations · 32 across the 6 of their papers we have counts for
6 papers
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…
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…
"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…
BuDDIE: A Business Document Dataset for Multi-task Information Extraction
Ran Zmigrod, Dongsheng Wang, Mathieu Sibue +10
The field of visually rich document understanding (VRDU) aims to solve a multitude of well-researched NLP tasks in a multi-modal domain. Several datasets exist for research on spec…
DocLLM: A layout-aware generative language model for multimodal document understanding
Dongsheng Wang, Natraj Raman, Mathieu Sibue +6
Enterprise documents such as forms, invoices, receipts, reports, contracts, and other similar records, often carry rich semantics at the intersection of textual and spatial modalit…
Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams
Ethan Callanan, Amarachi Mbakwe, Antony Papadimitriou +6
Large Language Models (LLMs) have demonstrated remarkable performance on a wide range of Natural Language Processing (NLP) tasks, often matching or even beating state-of-the-art ta…