Memory-augmented personalized agents for long-term dialogue

Abstract

In the field of open-domain dialogue systems, with the advent of large language models (LLMs), significant progress has been witnessed. However, most existing systems are designed for short, single-session interactions, falling short of meeting real-world needs for long-term companionship and personalized exchanges with chatbots. Addressing these demands requires robust event summarization and persona management, which enable reasoning for contextually appropriate responses in extended dialogues. Recent advancements in the cognitive and reasoning capabilities of LLMs highlight their potential to transform automated perception, decision-making, and problem-solving. This dissertation implements a memory-augmented personalized agent framework which integrates three independently modules focused on event memory, persona extraction, and response generation. The event memory module employs both long-term and short-term memory banks to handle historical and ongoing session data, respectively. A topic-based retrieval mechanism further improves the accuracy of memory retrieval. Meanwhile, the persona module dynamically models the personas of both users and agents, enabling nuanced interactions. Retrieved memories and extracted personas are then combined within the response generator to produce relevant and coherent outputs. Empirical evaluations demonstrate the effectiveness of this memory-augmented personalized agent framework, establishing it as a versatile solution for long-term dialogue scenarios.