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    MACHINE LEARNING MODEL EVALUATION
    11.
    发明申请
    MACHINE LEARNING MODEL EVALUATION 有权

    公开(公告)号：US20250005459A1

    公开(公告)日：2025-01-02

    申请号：US18885135

    申请日：2024-09-13

    Applicant: Lemon Inc.

    Inventor： Yuanshun Yao ,  Jiaheng Wei ,  Jean-Francois Ton ,  Hongyi Guo ,  Andrew Estornell ,  Yang Liu

    IPC: G06N20/00

    Abstract: Embodiments of the disclosure provide a solution for machine learning model evaluation. The solution includes: obtaining a target answer to a test question generated by a target machine learning (ML) model; obtaining a plurality of reference answers to the test question generated respectively by a plurality of reference ML models; determining respective professional levels of the plurality of reference ML models in answering the test question; and generating an evaluation result on correctness of the target ML model in question answering based on the target answer, the plurality of reference answers and the respective professional levels of the plurality of reference ML models.

    LANGUAGE PROCESSING
    12.
    发明申请
    LANGUAGE PROCESSING 有权

    公开(公告)号：US20240419980A1

    公开(公告)日：2024-12-19

    申请号：US18821829

    申请日：2024-08-30

    Applicant: Lemon Inc.

    Inventor： Yegor Klochkov ,  Yang Liu

    IPC: G06N3/0985

    Abstract: There are proposed methods, devices, and computer program products for language processing. In the method, a reference dataset is obtained, the reference dataset comprising a plurality of reference samples, a reference sample in the plurality of reference samples comprising: a reference text string and a reference label corresponding to the reference text string, the reference label indicating a processing result of the language processing. An influence of the reference dataset on a loss is determined, the loss being used for updating a language model associated with the language processing based on the plurality reference samples. A hyperparameter is determined for updating the language model based on the influence of the reference dataset. The language model is updated based on the hyperparameter, the loss, and the plurality of reference samples. Therefore, the language model may be updated in an accurate and effective way.

    BALANCING CLASSIFICATION ACCURACY AND FAIRNESS OF CLASSIFIER MODELS
    13.
    发明申请
    BALANCING CLASSIFICATION ACCURACY AND FAIRNESS OF CLASSIFIER MODELS 有权

    公开(公告)号：US20250111263A1

    公开(公告)日：2025-04-03

    申请号：US18396627

    申请日：2023-12-26

    Applicant: Lemon Inc.

    Inventor： Yegor Klochkov ,  Wenlong Chen ,  Yang Liu

    IPC: G06N20/00

    Abstract: The present disclosure describes techniques for balancing classification accuracy and fairness of a model trained to perform classification tasks. At least one bias score function corresponding to each sensitive attribute associated with instances classified by the model is configured. The at least one bias score function is configured to measure fairness on an instance level. At least one modification rule is generated based on the at least one bias score function and parameters. The at least one modification rule corresponds to at least one fairness criterion. The parameters are associated with a target level of the at least one fairness criterion. At least a subset of predictions are modified by applying the at least one modification rule to the predictions generated by the model. The modified predictions satisfy the target level of the at least one fairness criterion while maintaining the classification accuracy.

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