公开(公告)号：US20240338530A1

公开(公告)日：2024-10-10

申请号：US18745550

申请日：2024-06-17

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Zhen GUO ,  Wenquan WU ,  Hua WU ,  Haifeng WANG

IPC: G06F40/35 ,  G06N20/00

CPC classification number: G06F40/35 ,  G06N20/00

Abstract: A generative dialog model training method in the fields of artificial intelligence, such as deep learning, natural language processing, intelligent dialogs, is disclosed. The generative dialog model training method may include: in response to determination of an update of a safety specification, taking an updated safety specification as a target safety specification, and determining a dialog input corresponding to a current optimization according to the target safety specification, the update being performed on a previous safety specification when a generative dialog model after last optimization is determined not to meet a launch requirement; and optimizing the generative dialog model according to the dialog input and a principle that a reply generated by the generative dialog model conforms to the target safety specification, the generative dialog model being configured to generate the reply corresponding to the dialog input.

公开(公告)号：US20230075339A1

公开(公告)日：2023-03-09

申请号：US18056137

申请日：2022-11-16

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Zeyang LEI ,  Xinchao XU ,  Wenquan WU ,  Zhengyu Niu

IPC: G06F40/40 ,  G06F40/284

Abstract: The present disclosure provides a method of training an information generation model, a method of generating an information, an electronic device, and a storage medium. A specific implementation solution of the method of training the information generation model includes: splitting a description information for a target object in an information pair into at least one description word, so as to obtain a description word sequence, wherein the information pair further includes a first recommendation information; inputting the description word sequence into a dialog generation model to obtain a probability vector sequence for the target object, wherein each probability vector in the probability vector sequence includes probability values for a plurality of predetermined words; and training the dialog generation model according to the probability vector sequence and the first recommendation information, so as to obtain the information generation model.