Chinchilla vs GPT-4

Chinchilla is an advanced artificial intelligence model with 70 billion parameters, developed to optimize both model size and the volume of training data for efficient learning. It was trained using an extraordinary 1.4 trillion tokens, with an emphasis on scaling the model and data proportionately. This method of training is based on research that suggests optimal training occurs when model size and training tokens are increased in tandem. Chinchilla shares its compute budget with another model named Gopher, but it distinguishes itself by leveraging four times more training data. Despite this difference, both models are designed to operate under the same number of FLOPs, ensuring efficient compute resource utilization. Chinchilla leverages MassiveText, a vast dataset, and employs an adaptation of the SentencePiece tokenizer to interpret and process data. For a detailed understanding of its architecture and training, one can refer to the paper that elaborates on these aspects.

GPT-4 is the latest milestone in OpenAI’s effort in scaling up deep learning.

GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks. For example, it passes a simulated bar exam with a score around the top 10% of test takers; in contrast, GPT-3.5’s score was around the bottom 10%. We’ve spent 6 months iteratively aligning GPT-4 using lessons from our adversarial testing program as well as ChatGPT, resulting in our best-ever results (though far from perfect) on factuality, steerability, and refusing to go outside of guardrails.

GPT-4 is more creative and collaborative than ever before. It can generate, edit, and iterate with users on creative and technical writing tasks, such as composing songs, writing screenplays, or learning a user’s writing style.