Code intelligence-oriented cross-task shared optimization method and device, and electronic equipment

By combining hierarchical contextual learning with multimodal contrastive learning of code natural language and a shared parameter transfer strategy, the cross-task generalization ability and semantic alignment accuracy of large language models in the field of code intelligence are improved. This solves the problem of weak cross-task transfer ability of models in existing technologies and achieves efficient cross-task tuning and flexible adaptation.

CN122308837APending Publication Date: 2026-06-30WUHAN UNIV +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
WUHAN UNIV
Filing Date
2026-02-14
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing large language models in the field of code intelligence suffer from problems such as weak cross-task generalization ability, weak cross-task transfer generalization ability, and low semantic alignment accuracy between code and natural language. In particular, the model convergence speed is slow and the performance is limited in small sample scenarios.

Method used

By co-training hierarchical context learning and code natural language multimodal contrastive learning, combined with a shared parameter transfer strategy, freezing network parameters, and updating only prefix tuning parameters and low-rank matrix parameters, training data is generated and pre-trained to achieve the model's understanding and alignment of code hierarchy and cross-modal semantics.

Benefits of technology

It significantly improves the model's cross-task generalization ability in the field of code intelligence, reduces the tuning cost of downstream tasks, improves the model's understanding accuracy and semantic alignment ability of cross-level code structures, and achieves efficient cross-task tuning and flexible adaptation.

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Abstract

This application relates to the field of cross-task shared optimization technology for code intelligence, and particularly to a method, apparatus, and electronic device for cross-task shared optimization of code intelligence. The method includes: acquiring a hierarchical context learning task and a code-natural language multimodal contrastive learning task; generating training data based on the hierarchical context learning task and the code-natural language multimodal contrastive learning task; training a pre-set pre-trained language model based on the training data; freezing the network parameters of the trained pre-trained language model; transferring the shared parameters of the pre-trained language model to the downstream code target task for parameter optimization; inputting the code to be tested into the optimized pre-trained language model; and outputting the optimization result of the code to be tested through the pre-trained language model. This solves the problems of weak cross-task transfer and generalization ability of pre-trained models and low parameter optimization efficiency in related technologies.
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