公开(公告)号：US20250021891A1

公开(公告)日：2025-01-16

申请号：US18900432

申请日：2024-09-27

Applicant: Beijing Youzhuju Network Technology Co., Ltd. ,  Lemon Inc.

Inventor： Yuanshun Yao ,  Hongyi Guo ,  Xiaoying Zhang ,  Yang Liu

IPC: G06N20/00

Abstract: A method is proposed for machine learning (ML) model alignment. In the method, a first number of samples is generated by a target ML model based on samples selected from a set of samples. A sample comprises a question-answer pair. The set of samples is updated by adding at least a portion of the first number of samples to the set of samples. The target ML model is trained with at least a portion of the updated set of samples. In this way, the ML model self-generalization ability is unlocked to perform alignment with near-zero human supervision.

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.