Orca vs GPT-4

Recent advancements in artificial intelligence have paved the way for pioneering research in machine learning, specifically targeting the refinement of smaller AI models. Inspired by this notion, Microsoft Research introduces "Orca," a cutting-edge methodology designed to upgrade the learning processes of these compact AI constructs by utilizing the robust explanation traces produced by larger foundation models like GPT-4.

This innovative approach tackles several challenges encountered in the realm of AI imitation learning, such as the limited learning signals derived from the superficial outputs of larger models, the restrictive nature of small and homogeneous training datasets, and critically, the deficiency of stringent evaluation parameters that directly influence the overall quality and reliability of these models. Orca is a testament to Microsoft's commitment to evolving AI learning and potentiating smaller models' ability to reason, explain, and ultimately understand much like their large-scale counterparts.

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.