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公开(公告)号:US20250021610A1
公开(公告)日:2025-01-16
申请号:US18901831
申请日:2024-09-30
Inventor: Zeyang LEI , Siqi BAO , Hua WU , Haifeng WANG
IPC: G06F16/9535
Abstract: A human-machine interaction solution which relates to the field of artificial intelligence technologies, such as natural language processing technologies, large language models, deep learning technologies, or the like, is proposed. The solution may include: acquiring a question input by a user during a conversation with a large language model; retrieving memory information in a memory bank, the memory information being historical memory information about the user; and in response to retrieved memory information required for generating answer information corresponding to the question, taking the retrieved memory information as matched memory information, and generating the answer information by the large language model in conjunction with the matched memory information.
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公开(公告)号:US20250061311A1
公开(公告)日:2025-02-20
申请号:US18746532
申请日:2024-06-18
Inventor: Zeyang LEI , Siqi BAO , Hua WU , Haifeng WANG
IPC: G06N3/0475 , G06N3/08
Abstract: A data generation method is provided. The data generation method includes: generating first answer data based on first question data from a user; determining, in response to receiving negative feedback from the user for the first answer data, a first reflection result for the first answer data based on the first answer data and the negative feedback, wherein the first reflection result indicates a diagnosis reason why feedback from the user for the first answer data is negative; and generating second answer data for the first question data based on the first question data and the first reflection result.
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公开(公告)号:US20250103963A1
公开(公告)日:2025-03-27
申请号:US18969634
申请日:2024-12-05
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.
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公开(公告)号:US20250094789A1
公开(公告)日:2025-03-20
申请号:US18968810
申请日:2024-12-04
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.
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公开(公告)号:US20230214689A1
公开(公告)日:2023-07-06
申请号:US18121053
申请日:2023-03-14
Inventor: Xin TIAN , Yingzhan LIN , Mengfei SONG , Siqi BAO , Shiwei HUANG
IPC: G06N5/04
CPC classification number: G06N5/04
Abstract: A method for processing a dialogue includes: obtaining a dialogue text of the dialogue, in which the dialogue text includes a current question text, or the dialogue text includes the current question text and a historical dialogue text; extracting a current query text from the dialogue text; obtaining a knowledge query result for the current query text by querying a knowledge database based on the current query text; and determining a response text for the current question text based on the knowledge query result and the dialogue text.
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