The Evolution of Language Models: From Word2Vec to GPT-4

Understanding key papers that led to the invention of LLMs like GPT-4

Over the past decade, natural language processing (NLP) has undergone remarkable advancements, thanks to groundbreaking research and innovative techniques. From the initial development of Word2Vec to the emergence of large-scale pre-trained models like GPT and BERT, each step has significantly impacted the capabilities and applications of NLP systems. In this post, we explore the key research papers and ideas that have shaped the field, tracing the evolution from Word2Vec to GPT-4.

1. Word2Vec: Introduced the concept of learning word embeddings that capture semantic meaning by predicting surrounding words in a sentence.

   📃 Efficient Estimation of Word Representations in Vector Space

2. Seq2Seq: Built on word embeddings to develop the encoder-decoder architecture using RNNs for mapping input sequences to output sequences. 📃 Sequence to Sequence Learning with Neural Networks

3. Attention Mechanism: Improved seq2seq models by enabling networks to focus on relevant parts of the input when generating output. 📃 Neural Machine Translation by Jointly Learning to Align and Translate

4. Transformers: Introduced a novel NLP architecture that relied solely on attention mechanisms, discarding RNNs and CNNs. 📃 Attention is All You Need

5. GPT: Applied unsupervised pre-training and task-specific fine-tuning using the Transformer architecture to achieve impressive performance. 📃 Improving Language Understanding by Generative Pre-Training

6. BERT: Extended pre-training with masked language modeling, enabling bidirectional context learning and achieving state-of-the-art performance. 📃 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

7. T5: Adopted a unified text-to-text framework, demonstrating the importance of a unified approach for various NLP problems. 📃 Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

8. GPT-2: Increased model size and training data, demonstrating remarkable text generation abilities and raising ethical concerns. 📃 Language Models are Unsupervised Multitask Learners

9. GPT-3: Made a major leap forward with a larger model and more diverse training data, showcasing impressive few-shot learning capabilities. 📃 Language Models are Few-Shot Learners

10. LoRA: Addressed limitations of fine-tuning large-scale language models by introducing a low-rank adaptation technique, enabling efficient and effective fine-tuning. 📃 LoRA: Low-Rank Adaptation of Large Language Models

11. InstructGPT: Extended GPT-3 by training it to follow instructions, demonstrating improved performance on downstream tasks with fewer examples. 📃 Training language models to follow instructions with human feedback

12. GPT-4: The latest iteration, building on the successes of predecessors with further refinements and improvements, achieving state-of-the-art performance. 📃 GPT-4 Technical report