Microsoft Technology Licensing, LLC

Matthew Jonathan Gardner

Ido Priness

Shiraz Jitendra Cupala

Jaime Brooks Teevan

Adi L Miller

Amir Kantor

Lan Ye

Kevin Michael Carter

Shlomi Maliah

Shai Ifrach

Yasemin Cenberoglu

Kumud Ranjan

Shyam Gupta

Gautam Jain

Xiao Yan Lu

Gilad Gildin

Ofek David

Eleonora Shtotland

A system for providing a personalized assistant within a network-based communication session includes a processor and a memory storage device storing instructions. The system determines when a first communication session participant joins the network-based communication session after a threshold duration of time subsequent to the start time of the session. Upon determining the first participant has joined, the system obtains content associated with the session and creates request data for a pre-trained generative language model. The request data includes an instruction requesting a predetermined number of suggested utterances not present in the content, each utterance relating to one or more topics corresponding to the content. The system transforms the request data to a command based on a command template and provides the command to the generative language model. The system receives a response from the model, including the predetermined number of suggested utterances, and presents them to the communication session participant in a graphical user interface while the session is in session.

Network-based communication session copilot

Systems and methods are provided for implementing confidence enhancement for responses by document-based large language models ('LLMs') or other AI/ML systems. A first prompt is generated based on data items that are previously received or accessed. The first prompt is used by a first LLM or AI/ML system to extract requested information from the data items. One or more citations are generated and presented within a structured object together with a representation of the extracted information, in some cases, as output from a second LLM or AI/ML system. In some cases, the citations and/or the representation may be verified by a third LLM or AI/ML system, and reliability indicators may be generated for the citations and/or the representation based on determined accuracy of the citations and/or the representation. In this manner, the common issue of hallucinations may be mitigated.

Confidence enhancement for responses by document-based large language models

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Matthew Jonathan Gardner