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    TRANSLATION METHOD, CLASSIFICATION MODEL TRAINING METHOD, DEVICE AND STORAGE MEDIUM
    3.
    发明申请
    TRANSLATION METHOD, CLASSIFICATION MODEL TRAINING METHOD, DEVICE AND STORAGE MEDIUM 有权

    公开(公告)号：US20230015313A1

    公开(公告)日：2023-01-19

    申请号：US17656160

    申请日：2022-03-23

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

    Inventor： Chuanqiang Zhang ,  Ruiqing Zhang ,  Zhongjun He ,  Zhi Li ,  Hua Wu

    IPC: G06F40/58 ,  G06F40/279

    Abstract: Disclosed are a translation method, a classification model training method, a device and a storage medium, which relate to the field of computer technologies, particularly to the field of artificial intelligence such as natural language processing and deep learning. The translation method includes: obtaining a current processing unit of a source language text based on a segmented word in the source language text; determining a classification result of the current processing unit with a classification model; and in response to determining that the classification result is the current processing unit being translatable separately, translating the current processing unit to obtain translation result in a target language corresponding to the current processing unit.

    Model training
    5.
    发明授权
    Model training 有权

    公开(公告)号：US12277398B2

    公开(公告)日：2025-04-15

    申请号：US17694034

    申请日：2022-03-14

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

    Inventor： Jian Gong ,  Yu Sun ,  Hao Tian ,  Hua Wu ,  Haifeng Wang ,  Qiaoqiao She

    IPC: G06F40/40 ,  G06F40/205 ,  G06F40/284

    Abstract: A model training method, a model training platform, an electronic device and a storage medium are provided, which can be used in the field of artificial intelligence, particularly the fields of natural language processing and deep learning. The model training method includes: receiving an input; determining, based on the input, a user-oriented prefabricated function; determining, based on the input, a model training function; determining, based on the input, a pre-trained model; determining, based on the input, a network structure associated with the pre-trained model so as to support use of the pre-trained model; training, based on the input, the model by using the prefabricated function, the model training function, and the pre-trained model; and providing an output associated with a trained model.

    Text processing method
    6.
    发明授权
    Text processing method 有权

    公开(公告)号：US12277387B2

    公开(公告)日：2025-04-15

    申请号：US18056197

    申请日：2022-11-16

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

    Inventor： Ruiqing Zhang ,  Zhongjun He ,  Zhi Li ,  Hua Wu

    IPC: G06F40/232 ,  G06F40/279 ,  G06F40/53

    Abstract: A text processing method is provided. The method includes: a first probability value of each candidate character of a plurality of candidate characters corresponding to a target position is determined based on character feature information corresponding to the target position in a text fragment to be processed, wherein the character feature information is determined based on a context at the target position in the text fragment to be processed; a second probability value of each candidate character of the plurality of candidate characters is determined based on a character string including the candidate character and at least one character in at least one position in the text fragment to be processed adjacent to the target position; and a correction character at the target position is determined based on the first probability value and the second probability value of each candidate character of the plurality of candidate characters.

    LARGE LANGUAGE MODEL TRAINING METHOD, ELECTRONIC DEVICE, AND STORAGE MEDIUM
    7.
    发明申请
    LARGE LANGUAGE MODEL TRAINING METHOD, ELECTRONIC DEVICE, AND STORAGE MEDIUM 有权

    公开(公告)号：US20250094806A1

    公开(公告)日：2025-03-20

    申请号：US18967167

    申请日：2024-12-03

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

    Inventor： Junyuan Shang ,  Yilong Chen ,  Zhenyu Zhang ,  Shuohuan Wang ,  Yu Sun ,  Hua Wu

    IPC: G06N3/082 ,  G06N3/0475

    Abstract: Provided is a large language model training method, an electronic device and a storage medium, relating to the field of artificial intelligence technologies, and in particular, to the fields of deep learning, natural language processing and large model. The method includes: performing dimension reduction parameter fusion on a two-dimensional parameter matrix on each channel in each network layer in a first large language model, respectively, to obtain a second large language model; performing layer reduction parameter fusion on network layers in the second large language model based on a three-dimensional parameter matrix of each network layer in the second large language model to obtain a third large language model; and training the third large language model to obtain a target large language model under the condition that the target loss function determined based on the first and third large language models meets a preset first function condition.

    DIALOG METHOD AND SYSTEM, ELECTRONIC DEVICE AND STORAGE MEDIUM
    10.
    发明申请
    DIALOG METHOD AND SYSTEM, ELECTRONIC DEVICE AND STORAGE MEDIUM 有权

    公开(公告)号：US20230029687A1

    公开(公告)日：2023-02-02

    申请号：US17655772

    申请日：2022-03-21

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

    Inventor： Jun Xu ,  Zeming Liu ,  Zeyang Lei ,  Zhengyu Niu ,  Hua Wu ,  Haifeng Wang

    IPC: G06F40/35 ,  G06F40/56 ,  G06N5/02

    Abstract: The present disclosure provides a dialog method and system, an electronic device and a storage medium, and relates to the field of artificial intelligence (AI) technologies such as deep learning and natural language processing. A specific implementation scheme involves: rewriting a corresponding dialog state based on received dialog information of a user; determining to-be-used dialog action information based on the dialog information of the user and the dialog state; and generating a reply statement based on the dialog information of the user and the dialog action information. According to the present disclosure, the to-be-used dialog action information can be determined based on the dialog information of the user and the dialog state; and then the reply statement is generated based on the dialog action information, thereby providing an efficient dialog scheme.

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