Learning GenAI via SOTA Papers

The paper " Improving Language Understanding by Generative Pre-Training " by Alec Radford and colleagues at OpenAI introduces a semi-supervised framework to address the challenge of limited labeled data for diverse natural language understanding (NLU) tasks. The authors propose a two-stage training procedure : • Unsupervised Pre-training: A high-capacity 12-layer Transformer decoder is first train...

The paper " Deep contextualized word representations " introduces a novel type of word representation called ELMo (Embeddings from Language Models). Unlike traditional word embeddings that provide a single, context-independent vector for each word, ELMo representations are deep contextualized vectors derived from all internal layers of a deep bidirectional language model (biLM) pre-trained on a la...

The paper " Universal Language Model Fine-tuning for Text Classification " by Jeremy Howard and Sebastian Ruder introduces ULMFiT , an effective transfer learning method for Natural Language Processing (NLP). While transfer learning has long revolutionized computer vision, NLP models previously required significant task-specific modifications or training from scratch. ULMFiT enables ImageNet-like...

Attention is All You Need • The Shift to Transformers: The overview discusses the move away from complex recurrent and convolutional neural networks toward the Transformer architecture , which relies entirely on attention mechanisms to draw global dependencies between inputs and outputs,. • Self-Attention & Multi-Head Attention: It explains how the model uses self-attention to relate different...