Artificial Intelligence Language Model Based on Long-term Memory Enhancement Technology

This study explores the application of long-term memory enhancement technology in artificial intelligence language models. By integrating attention mechanisms, hierarchical network design, and dynamic pruning techniques, this research significantly enhances the model's ability to process long sequence texts. The new model demonstrates lower perplexity on multiple standard test sets, effectively proving the practical benefits of the adopted technologies. While there is room for improvement in handling unstructured text and high-noise data, the achieved results clearly showcase the immense potential of long-term memory enhancement technology in improving language models. Future work will focus on improving the model's computational efficiency and enhancing its adaptability in more complex application scenarios.