Marian vs GPT-4

Marian is a cutting-edge Neural Machine Translation (NMT) framework designed for speed and efficiency in research and production environments. Engineered entirely in C++, it features a self-contained architecture with an integrated automatic differentiation engine that leverages dynamic computation graphs. This design fosters flexibility and rapid experimentation, accommodating a wide array of NMT models including the encoder-decoder framework. Notably, Marian has been optimized to deliver remarkable training and translation speeds while maintaining accessibility and ease of use for researchers in the computational linguistics domain.

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.