Model training method, interaction method, device, equipment and medium

CN122153444APending Publication Date: 2026-06-05BEIJING BAIDU NETCOM SCI & TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING BAIDU NETCOM SCI & TECH CO LTD
Filing Date
2026-02-26
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing front-end code generation technologies are costly and perform poorly in specific application scenarios, making them difficult to generalize to a wide range of applications. Furthermore, their evaluation methods are difficult to diversify and target.

Method used

By inputting task information into the code generation model for evaluation, abnormal code is identified, and the model is trained using functional information from normal code and abnormal code. Vulnerabilities are injected to improve performance, and targeted training is carried out using a multi-dimensional evaluation strategy and hierarchical fine-grained training samples.

Benefits of technology

It improves the performance and training effect of code generation models in specific scenarios, enhances the running performance and interactive experience of generated code, and meets the diversity and personalization of user needs.

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Abstract

The present disclosure provides a model training method, an interaction method, an apparatus, an agent, an electronic device, a storage medium and a program product, relates to the technical field of artificial intelligence, and in particular to the technical field of deep learning technology, model training technology, software technology and the like. The specific implementation scheme is as follows: inputting task information into a code generation model to obtain task code; evaluating the task code to obtain an evaluation result; in the case that the evaluation result indicates that the task code is abnormal, updating normal code for improving the performance of the code generation model based on the evaluation result to obtain abnormal code injected with a vulnerability; and training the code generation model by using code function information of the normal code, the normal code and the abnormal code.
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