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    QUERY ANSWERING METHOD BASED ON LARGE MODEL, ELECTRONIC DEVICE, STORAGE MEDIUM, AND INTELLIGENT AGENT
    21.
    发明申请
    QUERY ANSWERING METHOD BASED ON LARGE MODEL, ELECTRONIC DEVICE, STORAGE MEDIUM, AND INTELLIGENT AGENT 有权

    公开(公告)号：US20250094460A1

    公开(公告)日：2025-03-20

    申请号：US18969597

    申请日：2024-12-05

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

    Inventor： Haifeng WANG ,  Hua WU ,  Hao TIAN ,  Jing LIU ,  Ruiqing ZHANG ,  Yan CHEN ,  Yu RAN

    IPC: G06F16/3329 ,  G06F16/3332 ,  G06F16/334

    Abstract: A query answering method, an electronic device, a storage medium, and an intelligent agent are provided, which relate to a field of artificial intelligence technology, and in particular to fields of large model, intelligent search and information processing technology. The method includes: inputting, in response to a retrieval content set retrieved based on a query, the query, the retrieval content set and prompt information for answer generation into the large model, so that the large model performs operations of: processing, based on a current task in the prompt information and the query, a current text corresponding to the retrieval content set to obtain a processed text, where the current task is determined based on a task execution order in the prompt information; and obtaining, in a case of determining that the processed text meets a preset condition, an answer to the query based on the processed text.

    TRAINING METHOD FOR A DEEP LEARNING MODEL
    22.
    发明申请
    TRAINING METHOD FOR A DEEP LEARNING MODEL 有权

    公开(公告)号：US20250061305A1

    公开(公告)日：2025-02-20

    申请号：US18936686

    申请日：2024-11-04

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

    Inventor： Shuohuan WANG ,  Junyuan SHANG ,  Yinqi YANG ,  Guoxia WANG ,  Linhao ZHANG ,  Yu SUN ,  Hua WU ,  Haifeng WANG

    IPC: G06N3/043 ,  G06N3/045 ,  G06N3/0985

    Abstract: A training method, an inference method, a device, an apparatus, and a medium for a deep learning model are provided. A first model includes a plurality of first parameters, a second model comprises a plurality of second parameters, which is initialized to parameter values of a plurality of target parameters selected from the plurality of first parameters. The training method includes: determining a target loss for both the first model and the second model; adjusting parameter values, including: in response to determining that the target loss indicates that the parameter values of at least part of the target parameters need to be adjusted, synchronously adjusting the parameter values of the corresponding second parameters; and in response to determining that the target loss indicates that the parameter values of at least part of the second parameters need to be adjusted, synchronously adjusting the parameter values of the corresponding target parameters.

    METHOD AND APPARATUS FOR TRAINING QUESTION SOLVING MODEL, QUESTION SOLVING METHOD AND APPARATUS
    23.
    发明公开
    METHOD AND APPARATUS FOR TRAINING QUESTION SOLVING MODEL, QUESTION SOLVING METHOD AND APPARATUS 审中-公开

    公开(公告)号：US20240354658A1

    公开(公告)日：2024-10-24

    申请号：US18745529

    申请日：2024-06-17

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

    Inventor： Feng HE ,  Jianhua WANG ,  Junjie OU ,  Pingxuan HUANG ,  Zhifan FENG ,  Xiaopeng CUI ,  Qiaoqiao SHE ,  Hua WU

    IPC: G06N20/00 ,  G06N5/04

    CPC classification number: G06N20/00 ,  G06N5/04

    Abstract: A method and apparatus for training a question solving model, a question solving method and apparatus, an electronic device and a readable storage medium are disclosed. The method for training a question solving model includes: acquiring a first sample question; inputting the first sample question and a solving step grabbing template into a large language model to obtain a first sample solving step; inputting the first sample question, the first sample solving step and an answer grabbing template into the large language model to obtain a first sample answer; pre-training a step planning model according to the first sample question and the first sample solving step; pre-training the large language model according to the first sample question, the first sample solving step and the first sample answer; and acquiring the question solving model according to the step planning model and the large language model obtained by pre-training. The question solving method includes: acquiring a to-be-solved question; inputting the to-be-solved question into a step planning model to obtain a solving step; and inputting the to-be-solved question and the solving step into a large language model to obtain an answer.

    METHOD FOR TRAINING NON-AUTOREGRESSIVE TRANSLATION MODEL
    27.
    发明申请
    METHOD FOR TRAINING NON-AUTOREGRESSIVE TRANSLATION MODEL 有权

    公开(公告)号：US20230051373A1

    公开(公告)日：2023-02-16

    申请号：US17974317

    申请日：2022-10-26

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

    Inventor： Xiyang WANG ,  Ruiqing ZHANG ,  Zhongjun HE ,  Zhi LI ,  Hua WU

    IPC: G06F40/47

    Abstract: A method for training a non-autoregressive translation (NAT) model includes: acquiring a source language text, a target language text corresponding to the source language text and a target length of the target language text; generating a target language prediction text and a prediction length by inputting the source language text into the NAT model, in which initialization parameters of the NAT model are determined based on parameters of a pre-trained translation model; and obtaining a target NAT model by training the NAT model based on the target language text, the target language prediction text, the target length and the prediction length.

    METHOD FOR TRAINING DECISION-MAKING MODEL PARAMETER, DECISION DETERMINATION METHOD, ELECTRONIC DEVICE, AND STORAGE MEDIUM
    28.
    发明申请
    METHOD FOR TRAINING DECISION-MAKING MODEL PARAMETER, DECISION DETERMINATION METHOD, ELECTRONIC DEVICE, AND STORAGE MEDIUM 有权

    公开(公告)号：US20230032324A1

    公开(公告)日：2023-02-02

    申请号：US17966127

    申请日：2022-10-14

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

    Inventor： Fan WANG ,  Hao TIAN ,  Haoyi XIONG ,  Hua WU ,  Jingzhou HE ,  Haifeng WANG

    IPC: G06N20/00

    Abstract: A method for training a decision-making model parameter, a decision determination method, an electronic device, and a non-transitory computer-readable storage medium are provided. In the method, a perturbation parameter is generated according to a meta-parameter, and first observation information of a primary training environment is acquired based on the perturbation parameter. According to the first observation information, an evaluation parameter of the perturbation parameter is determined. According to the perturbation parameter and the evaluation parameter thereof, an updated meta-parameter is generated. The updated meta-parameter is determined as a target meta-parameter, when it is determined, according to the meta-parameter and the updated meta-parameter, that a condition for stopping primary training is met. According to the target meta-parameter, a target memory parameter corresponding to a secondary training task is determined, where the target memory parameter and the target meta-parameter are used to make a decision corresponding to a prediction task.

    AFFINITY PREDICTION METHOD AND APPARATUS, METHOD AND APPARATUS FOR TRAINING AFFINITY PREDICTION MODEL, DEVICE AND MEDIUM
    30.
    发明申请
    AFFINITY PREDICTION METHOD AND APPARATUS, METHOD AND APPARATUS FOR TRAINING AFFINITY PREDICTION MODEL, DEVICE AND MEDIUM 有权

    公开(公告)号：US20220215899A1

    公开(公告)日：2022-07-07

    申请号：US17557691

    申请日：2021-12-21

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

    Inventor： Fan WANG ,  Jingzhou HE ,  Xiaomin FANG ,  Xiaonan ZHANG ,  Hua WU ,  Tian WU ,  Haifeng WANG

    IPC: G16B15/30 ,  G16B40/00

    Abstract: The present disclosure discloses an affinity prediction method and apparatus, a method and apparatus for training an affinity prediction model, a device and a medium, and relates to the field of artificial intelligence technologies, such as machine learning technologies, smart medical technologies, or the like. An implementation includes: collecting a plurality of training samples, each training sample including information of a training target, information of a training drug and a test data set corresponding to the training target; and training an affinity prediction model using the plurality of training samples. In addition, there is further disclosed the affinity prediction method. The technology in the present disclosure may effectively improve accuracy and a training effect of the trained affinity prediction model. During an affinity prediction, accuracy of a predicted affinity of a target to be detected with a drug to be detected may be higher by acquiring a test data set corresponding to the target to be detected to participate in the prediction.

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