Memory-Augmented Architecture for Long-Term Context Handling in Large Language Models

TL;DR

This paper introduces a memory-augmented architecture for large language models that enhances their ability to maintain long-term context, leading to more coherent interactions and improved response quality.

Contribution

It presents a novel dynamic memory system that retrieves, updates, and prunes information to handle long-term context in large language models.

Findings

Significantly improves contextual coherence in dialogues

Reduces memory overhead compared to baseline models

Enhances response quality in real-time applications

Abstract

Large Language Models face significant challenges in maintaining coherent interactions over extended dialogues due to their limited contextual memory. This limitation often leads to fragmented exchanges and reduced relevance in responses, diminishing user experience. To address these issues, we propose a memory-augmented architecture that dynamically retrieves, updates, and prunes relevant information from past interactions, ensuring effective long-term context handling. Experimental results demonstrate that our solution significantly improves contextual coherence, reduces memory overhead, and enhances response quality, showcasing its potential for real-time applications in interactive systems.