ILSTMA: Enhancing Accuracy and Speed of Long-Term and Short-Term Memory Architecture

Abstract

In recent years, the rapid development of large language models (LLMs) has led to a growing consensus in the industry regarding the integration of long-term and short-term memory. However, the widespread application of long-term and short-term memory systems faces two significant challenges: increased execution time and decreased answer accuracy from LLMs. To tackle these challenges, we propose the ILSTMA. This architecture uniquely combines fundamental theories of human forgetting with classical operating system principles, providing an unprecedented acceleration method that does not rely on traditional memory retrieval algorithms, which is all based on the systematic planning of available memory space. Furthermore, our proposed most relevant dialogue retrieval process substantially enhances the answer accuracy of LLMs while examining the potential of the two most commonly used memory retrieval algorithms. Experimental results demonstrate that our acceleration method improves the execution efficiency of the original architecture by 21.45%, and our most relevant dialogue retrieval process raises the answer accuracy to 88.4%, surpassing several benchmarks. These findings validate the high performance of the ILSTMA.

Keywords

• long-term memory
• short-term memory
• LLM
• chat scene