XLNet vs GPT-4

XLNet is a ground-breaking unsupervised language pretraining approach developed by researchers, including Zhilin Yang and Zihang Dai. It introduces a Generalized Autoregressive Pretraining method that allows for state-of-the-art performance on various language understanding tasks. As an improvement over its predecessor, BERT, XLNet incorporates the Transformer-XL architecture, which is particularly adept at handling long-range dependencies in text. This repository, managed by Zihang Dai on GitHub, offers access to the XLNet model, with supporting code and documentation for researchers and AI practitioners to utilize and potentially contribute to the ongoing progress in language models.

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.