This paper provides a comprehensive analysis of tokenization in large language models, exploring the fundamental mechanisms that enable LLMs to process and understand text. The research examines various tokenization strategies, their impact on model performance, and the implications for natural language processing tasks.
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers.
Breaking down BPE with a fun, step-by-step example that you can actually try yourself.
A deep dive into Retrieval-Augmented Generation (RAG) and how it's revolutionizing AI responses by combining context-aware retrieval with sophisticated text generation.
Models with just 135M to 7B parameters started outperforming their heavyweight counterparts on real-world tasks. Learn why smaller is smarter and how SLMs deliver 10x cost reduction with 5x faster inference.