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:
Nov. 26, 2024

Filed:

Aug. 02, 2021
Applicant:

Naver Corporation, Seongnam-si, KR;

Inventors:

Marc Dymetman, Grenoble, FR;

Hady Elsahar, Grenoble, FR;

Muhammad Khalifa, Grenoble, FR;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/40 (2020.01); G06F 40/10 (2020.01); G06F 40/284 (2020.01); G06F 40/30 (2020.01); G06N 3/08 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06F 40/40 (2020.01); G06F 40/10 (2020.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01);
Abstract

A method for generating a language model for text generation by receiving a pre-trained language model having attributes with existing probability distributions over the pre-trained language model; receiving at least one target constraint; the target constraint specifying an expectation of a target attribute over a language model that approximates the pre-trained language model; computing parameters of an energy based model by applying the target constraint to the pre-trained language model; obtaining samples from a reference policy; updating parameters of a target policy using the obtained samples and the energy based model; updating the reference policy with the target policy if the target policy is superior to the reference policy; and outputting the target policy as a target language model. The target language model is adapted to generate text with the target attribute over a probability distribution that approximates the desired probability distribution specified by the target constraint.


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