Transaction processing method and device, storage medium and electronic equipment

By using a model training method with cross attention mechanism and backpropagation algorithm in large language models, the problem of inaccurate response of large language models in transaction processing scenarios is solved, significantly improving application effect and user experience.

CN119647600APending Publication Date: 2025-03-18ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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Patent Information

Application Number
CN202411756512.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-02
Publication Date
2025-03-18

AI Technical Summary

Technical Problem

Large language models have problems such as inaccurate response, hallucination generation and difficulty in following user requests in transaction processing scenarios, resulting in poor user experience and limited application potential.

Method used

By creating an initial transaction processing model based on the basic model, and using cross-attention mechanism and backpropagation algorithms during model training, model parameters are adjusted to improve response accuracy and the ability to align user needs.

Benefits of technology

It significantly improves the application effect and user experience of large language models in complex transaction scenarios, reduces the generation of illusions and deviations from user requests, and makes the model output more in line with the user's intentions.

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Abstract

The invention discloses a transaction processing method and device, a storage medium and electronic device.The method comprises the steps that an initial transaction processing large model is created based on a basic large model, a sample user query prompt is input into the initial transaction processing large model for model training, and in the forward propagation model training process, a user query prompt is input into the initial transaction processing large model; the method comprises the following steps: performing transaction preprocessing on a sample user query prompt through an initial transaction processing large model to obtain a reference processing result and a sample reference query prompt, and performing transaction processing by adopting a cross attention mechanism based on the sample user query prompt and the sample reference query prompt to obtain a prediction processing result; and determining model loss based on the prediction processing result, the reference processing result, the sample user query prompt and the sample reference query prompt, and performing model parameter adjustment on the initial transaction processing large model based on the model loss to obtain the transaction processing large model.
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