The article provides a comprehensive review of the evolution of generative pre-trained transformer (GPT) models, focusing on the differences between them. It discusses the original GPT model, GPT-2, GPT-3, Jurassic-1, Megatron-Turing NLG, Gopher, Chinchilla, PaLM, LLaMa, and GPT-4. The author highlights the architectural changes, training methods, and computational details of each model, noting that many papers do not provide enough detail to understand what drives their improvements. The author also critiques the lack of novelty in some models and the overemphasis on model size over dataset size. The article concludes with a call for more detailed information about GPT-4, which OpenAI has not yet released.
Key takeaways:
The article provides a comprehensive review of the evolution of generative pre-trained transformer (GPT) models, focusing on the differences between them.
The author discusses the architecture and training details of various models including GPT, GPT-2, GPT-3, Jurassic-1, Megatron-Turing NLG, Gopher, Chinchilla, PaLM, LLaMa, and GPT-4.
Chinchilla is highlighted as a particularly influential paper that established scaling laws and is often used as a reference for training large language models (LLMs).
The author notes a lack of information about GPT-4 due to OpenAI's decision not to release detailed information about its architecture, hardware, training compute, dataset construction, and training method.
