公开(公告)号：US20250103963A1

公开(公告)日：2025-03-27

申请号：US18969634

申请日：2024-12-05

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

Inventor： Xin TIAN ,  Yang YANG ,  Ji LUO ,  Huang HE ,  Siqi BAO ,  Bingjin CHEN ,  Hua WU

IPC: G06N20/00

Abstract: A method for processing a query-response information is provided, which relates to a field of artificial intelligence technology, and in particular to fields of deep learning, large models, intelligent query and response, etc. The method for processing a query-response information includes: generating at least one initial response information according to a query information provided by an object; acquiring at least one feedback information corresponding to the at least one initial response information, wherein the feedback information indicates a preference degree of the object for the initial response information; and generating a training sample according to the query information, the at least one initial response information and the at least one feedback information. The present disclosure further provides a method for training a conversational model, an electronic device, and a storage medium.

公开(公告)号：US20250094789A1

公开(公告)日：2025-03-20

申请号：US18968810

申请日：2024-12-04

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

Inventor： Hua LU ,  Shilong FAN ,  Zeyang LEI ,  Bingjin CHEN ,  Siqi BAO ,  Hua WU

IPC: G06N3/0475

Abstract: A method for evaluating a large model, an electronic device and a computer readable storage medium are provided, which relate to a field of artificial intelligence technology, and in particular to fields of large models technology and deep learning technology. The method includes: evaluating a response information of each of M large language models for an input instruction based on a preset evaluation rule, so as to obtain a first evaluation information for each response information, where M is a positive integer greater than 1; evaluating, in response to the first evaluation information for the M large language models being consistent with each other, each response information in a plurality of evaluation dimensions, so as to obtain a second evaluation information for each response information; and determining an evaluation result representing a responsiveness of each large language model, according to the second evaluation information for each response information.