We explore LLM applications for page stream segmentation in insurance document processing, demonstrating that parameter-efficient fine-tuning achieves strong accuracy but revealing significant calibration challenges that limit deployment confidence.
We create TabMe++, an enhanced page stream segmentation benchmark with commercial-grade OCR, and show that parameter-efficiently fine-tuned decoder-based LLMs like Mistral-7B achieve 80% straight-through processing rates, dramatically outperforming encoder-based models.
A computational social science project that engineered a custom extraction engine to build a 47,000+ bill knowledge graph from Congress.gov (115th-117th Congresses), creating a novel legislative graph with co-sponsorship networks and establishing an 87% accuracy benchmark for policy area classification now available on Hugging Face.