Microsoft Opens Source GODEL Language Model for Realistic Conversations

Creating better language models to help people in various aspects of life continues to be an important area of ​​activity for large technology companies. In fact, there have been so many advances in this area that even Google engineers have begun to believe that the company’s artificial intelligence has become sentient. Meanwhile, Amazon is trying to get Alexa to imitate the voice of your deceased relative.

Now Microsoft has announced that it is opening the source code of its Grounded Open Dialogue Language Model (GODEL) for use by a wider audience.

GODEL combines two important features in one language model. First, focus on the task, and second, make the conversation realistic and social. Typically, chatbots either try to be purposeful like help desk assistants or just try to communicate without a purpose.

GODEL is a class of pre-trained models that basically allows a conversational agent such as a chatbot to provide answers based on the current conversation and also generate external and related information to make your interaction more realistic and informed. The example provided by Microsoft is a restaurant recommendations talk that discusses recipes, cooking methods, and more.

Microsoft wants GODEL to be unrestricted as to the queries it can answer and expand on, but at the same time make the overall conversation useful.

In fact, GODEL is an evolution of DialoGPT that Microsoft Research released in 2019 . Microsoft’s automated human testing has shown its latest conversational AI to be far superior to DialoGPT, so the firm thinks it’s ready for wider use in a variety of environments. Below you can see an example of a conversation with GODEL in which he answers a question about a current event that was not part of his training set:

GODEL is now available as an open source repository on GitHub here, and you can also check out the related research article here . It comes in three flavors, along with the code you’ll need to retrain GODEL on a specific set of information.

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