#nlp
15 articles
- Attention Is All You Need The 2017 paper that introduced the Transformer architecture
- BERT: Bidirectional Transformers Pre-training deep bidirectional representations for NLP
- Chain-of-Thought Prompting Eliciting step-by-step reasoning in language models for complex problem solving
- CLIP: Contrastive Language-Image Pre-training Learning visual concepts from natural language supervision
- GPT: Generative Pre-Training Autoregressive language models that learn to predict the next token
- In-Context Learning How large language models learn from examples in the prompt without weight updates
- Neural Machine Translation by Jointly Learning to Align and Translate The paper that introduced the attention mechanism for sequence-to-sequence models
- RLHF: Reinforcement Learning from Human Feedback Teaching language models to prefer responses that people rank higher
- Scaling Laws for Neural Language Models Why bigger models, more data, and more compute lead to predictable gains
- Sequence to Sequence Learning Encoder-decoder architecture for mapping sequences to sequences
- The Annotated Transformer Line-by-line PyTorch implementation of the Transformer architecture
- The Unreasonable Effectiveness of Recurrent Neural Networks Andrej Karpathy's influential blog post demonstrating RNN capabilities through character-level generation
- Transformer Self-attention models that process sequences in parallel
- Understanding LSTM Networks Christopher Olah's visual guide to Long Short-Term Memory networks
- Word2Vec: Word Embeddings Learning dense vector representations of words from text