Andrew Ng's newly released document extraction service shows significant limitations when processing complex financial statements, with high error rates and slow processing times. Tests revealed over 50% hallucinated values and frequent missing data in financial tables, highlighting the challenges of using LLMs for document extraction.
VLM Run Hub offers pre-defined Pydantic schemas for extracting structured data from visual content using Vision Language Models, featuring industry-specific templates and automatic data validation. The platform supports multiple VLM providers and includes comprehensive documentation for seamless integration across various use cases.
Large Language Models (LLMs) face significant limitations in OCR tasks due to their probabilistic nature and inability to maintain precise visual information, particularly struggling with complex layouts and tables. LLMs' vision processing architecture leads to critical errors in data extraction, including financial and medical data corruption, while also being susceptible to prompt injection vulnerabilities.