Abstract
The intelligent judicial trial assistance system is an intelligent auxiliary judicial trial tool based on generative artificial intelligence and machine learning technology. It can achieve auxiliary functions such as case upload, trial result prediction, and legal document generation through automated, digital, and informational means. It utilizes technologies such as Recurrent Neural Networks (RNN) and Long Short-Term Memory networks (LSTM) to train generative models for generating trial recommendations and assisting in decision-making. Through in-depth research on the working principles and optimization paths of the intelligent judicial trial assistance system, the best applicable path for the system in judicial trial practice is explored. By establishing optimized models, risks and biases are minimized to the greatest extent.
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