Training method, device and system of large model-based risk identification system
By fine-tuning the large language model and jointly training the risk identification model, and adjusting only the parameters of the fine-tuning layer, a risk identification system is constructed, which solves the problem of low iteration efficiency in anti-fraud defense and achieves efficient and accurate risk identification.
CN122264168APending Publication Date: 2026-06-23BEIJING BAIDU NETCOM SCI & TECH CO LTD
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Patent Information
- Application Number
- CN202610195089.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-02-10
- Publication Date
- 2026-06-23
AI Technical Summary
Technical Problem
Existing technologies have low iteration efficiency in anti-fraud defense and are difficult to adapt to the rapid evolution of black market technologies, resulting in low anti-fraud defense efficiency.
Method used
A large language model is fine-tuned and combined with a risk identification model for training. Only the parameters of the fine-tuning layer are adjusted to build a risk identification system and improve risk identification capabilities through intent information.
Benefits of technology
It improves training efficiency and the accuracy of the recognition system, effectively identifying risks in complex environments and adapting to rapidly changing cheating methods.
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Figure CN122264168A_ABST
Abstract
The present disclosure discloses a training method, device and system of a large model-based risk identification system, relating to the technical field of artificial intelligence such as machine learning and natural language processing. The specific implementation scheme comprises: adopting a first training sample, fine-tuning a pre-trained large language model, so that the large language model learns to identify the intent information of the query sequence of the user; adopting a second training sample, training a risk identification system including the fine-tuned large language model and a risk identification model, so that the risk identification system learns to identify the risk information of the user; wherein during the training process, only the parameters of the fine-tuning layer in the fine-tuned large language model are adjusted.
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