6 papers
Can You Trust the Confidence? ConfBench for Vision-Language Models on Document Extraction
Priyashree Roy, Sujitha Martin, Mohammad Rostami +6
Intelligent document processing (IDP) with vision-language models (VLMs) hinges on confidence scores trustworthy enough to route extractions between automation and human review. Ex…
IDP AutoOpt: Agent-Driven Optimization of Document Processing Pipeline Configurations
David Kaleko, Sergey Ivanov, Md Mofijul Islam
We present IDP AutoOpt, an autonomous LLM agent that discovers high-performing configurations for intelligent document processing (IDP) pipelines. Tuning IDP prompts, models, OCR s…
IDP Accelerator: Agentic Document Intelligence from Extraction to Compliance Validation
Md Mofijul Islam, Md Sirajus Salekin, Joe King +8
Understanding and extracting structured insights from unstructured documents remains a foundational challenge in industrial NLP. While Large Language Models (LLMs) enable zero-shot…
DocSplit: A Comprehensive Benchmark Dataset and Evaluation Approach for Document Packet Recognition and Splitting
Md Mofijul Islam, Md Sirajus Salekin, Nivedha Balakrishnan +6
Document understanding in real-world applications often requires processing heterogeneous, multi-page document packets containing multiple documents stitched together. Despite rece…
MathMist: A Parallel Multilingual Benchmark Dataset for Mathematical Problem Solving and Reasoning
Mahbub E Sobhani, Md. Faiyaz Abdullah Sayeedi, Tasnim Mohiuddin +2
Mathematical reasoning remains one of the most challenging domains for large language models (LLMs), requiring not only linguistic understanding but also structured logical deducti…
Ready to Translate, Not to Represent? Bias and Performance Gaps in Multilingual LLMs Across Language Families and Domains
Md. Faiyaz Abdullah Sayeedi, Md. Mahbub Alam, Subhey Sadi Rahman +5
The rise of Large Language Models (LLMs) has redefined Machine Translation (MT), enabling context-aware and fluent translations across hundreds of languages and textual domains. De…