The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 23, 2025

Filed:

Apr. 12, 2023
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

Yang Liu, Bellevue, WA (US);

Yichong Xu, Bellevue, WA (US);

Dan Iter, Austin, TX (US);

Chenguang Zhu, Bellevue, WA (US);

Nanshan Zeng, Bellevue, WA (US);

Shuohang Wang, Belevue, WA (US);

Hiteshi Sharma, San Jose, CA (US);

Assignee:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/40 (2020.01); G06F 40/186 (2020.01); G06F 40/20 (2020.01); G06F 40/35 (2020.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06F 40/40 (2020.01); G06F 40/186 (2020.01); G06F 40/20 (2020.01); G06F 40/35 (2020.01); G06N 20/00 (2019.01);
Abstract

The techniques described herein enhance the operations of natural language generation systems through training and/or augmentation by a large language model. In a first example, the large language model can execute training operations by processing a training dataset to produce a natural language output. The natural language generation system can analyze the training dataset and the natural language output to generate a natural language output mimicking the output of the large language model. The large language model can then evaluate the output of the natural language generation system to iteratively adjust and improve the quality of natural language outputs. In a second example, the large language can augment a small language model in executing natural language tasks. This is accomplished by retrieving external information using the large language model to generate an augmentation input to provide context and a language framework to the small language model to enhance overall outputs.


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