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.
A comprehensive guide detailing the implementation of Llama3 from scratch, covering model architecture, attention mechanisms, and optimization techniques like KV-Cache, with detailed code explanations and mathematical derivations.
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.
Andrej Karpathy's deep dive into LLMs covers the complete lifecycle from pretraining to post-training, explaining tokenization, neural network architectures, and fine-tuning processes. The comprehensive guide explores how LLMs process information, handle hallucinations, and utilize reinforcement learning to improve performance and reasoning capabilities.