Pythia vs GPT-4

Pythia is an extensive suite designed to analyze the development and scaling of large language models (LLMs). It comprises 16 models, each trained with public data in a consistent order, featuring sizes ranging from 70 million to 12 billion parameters. The project provides public access to 154 checkpoints per model, as well as tools to reproduce training data loaders for in-depth research. Pythia aims to aid various research domains with case studies on topics like memorization, the impact of term frequency on few-shot learning, and strategies for mitigating gender bias. This carefully controlled environment offers unique insights into the training dynamics of LLMs. The suite includes trained models, analysis code, training code, and data, all accessible via its GitHub repository.

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.