UniLM vs ChatGPT Plugins

This paper introduces UniLM, a Unified pre-trained Language Model, that serves as a new benchmark for Natural Language Understanding (NLU) and Natural Language Generation (NLG) tasks. It is unique in its use of a shared Transformer network that is pre-trained on unidirectional, bidirectional, and sequence-to-sequence tasks, employing special self-attention masks for contextual prediction control. UniLM outperforms BERT in the GLUE benchmark and excels in SQuAD 2.0 and CoQA question answering, setting new records in five NLG datasets, including notable improvements in CNN/DailyMail and Gigaword summarization tasks. The models and code shared by the authors aid the research community in further advancements.

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.