Method and apparatus for training model, and device, medium and program product

By utilizing the feature descriptions and samples of the first model, a lightweight second model is generated and trained, solving the problems of long sample creation time and low model accuracy, and achieving efficient and accurate model training and fast response.

WO2026123304A1 Publication Date: 2026-06-18BEIJING ZITIAO NETWORK TECH CO LTD
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Patent Information

Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
BEIJING ZITIAO NETWORK TECH CO LTD
Filing Date
2024-12-12
Publication Date
2026-06-18

AI Technical Summary

⚠Technical Problem

In existing technologies, creating high-quality samples requires a lot of manual time, lightweight models have low accuracy, and large language models have too many parameters, making it impossible to achieve both high accuracy and small size at the same time.

⚗Method used

By utilizing the feature descriptions of the first model and the first samples, second samples are generated and trained. The knowledge of the first model is then transferred to the lightweight second model, improving training efficiency and accuracy.

🎯Benefits of technology

It enables the training of high-quality, lightweight models with a small number of samples, improving training efficiency and prediction accuracy while ensuring rapid response.

✦ Generated by Eureka AI based on patent content.

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Abstract

Provided in the embodiments of the present disclosure are a method and apparatus for training a model, and a device, a storage medium and a computer program product. The method comprises: acquiring a first sample and a feature description corresponding to the first sample. The method further comprises: on the basis of the feature description and the first sample, using a first model to determine a second sample. The method further comprises: on the basis of the feature description, using the first sample and the second sample to train a second model, wherein the number of parameters of the first model is greater than the number of parameters of the second model. In the method in the embodiments of the present disclosure, the first model can be used to supplement training samples on the basis of the provided feature description and the first sample, such that the second model can be trained by means of providing only a small number of samples, and the second sample is also enabled to contain knowledge of the first model. In this way, by using the first sample, the feature description and the second sample to train the second model, the knowledge of the first model can be transferred to the second model, thereby improving the training efficiency and prediction accuracy of the second model.
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