XLNet vs ChatGPT Plugins

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.

OpenAI follows an iterative deployment philosophy, and as part of this approach, it is gradually releasing plugins for ChatGPT. The purpose of this gradual release is to study the real-world use of the plugins, assess their impact, and identify any safety and alignment challenges that may arise. Getting these aspects right is essential for OpenAI to achieve its mission.

Since the launch of ChatGPT, users have expressed interest in having access to plugins, and many developers are experimenting with similar ideas. OpenAI is initially rolling out plugins to a small set of users and plans to gradually increase access as it learns more. After an alpha period, API users who want to integrate plugins into their products will also have access. OpenAI is excited to build a community that will help shape the future of the human-AI interaction paradigm.

Developers who have been invited off the waitlist can use OpenAI's documentation to build a plugin for ChatGPT. Once the plugin is created, it is listed in the prompt shown to the language model, along with documentation that instructs the model on how to use each one. The first set of plugins has been created by Expedia, FiscalNote, Instacart, KAYAK, Klarna, Milo, OpenTable, Shopify, Slack, Speak, Wolfram, and Zapier.