Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations

Maintaining long-term conversations has always been a long-term pursuit in current open-domain dialogue systems (Liu et al., 2016; Zhang et al., 2018; Kann et al., 2022; Song et al., 2023), commonly known as chatbots or conversational agents. Long-term conversation refers to the ability of a conversational agent to engage in extended dialogues over multiple interactions, often spanning several days or weeks even months. This setting is challenging because it necessitates not only a deep understanding of the immediate dialogue context but also the retention and integration of key information from past interactions. Effective long-term conversation requires a system to memorize or recall past dialogue snippets, contextual nuances, and user preferences, which are crucial for maintaining coherence and relevance in ongoing interactions (Wu et al., 2022; Zhang et al., 2022).

To acquire useful information from past conversations, the most mainstream approach in the field of long-term conversation currently is retrieval based methods, as illustrated in Figure 1 (a): Firstly, previous works (Xu et al., 2022b; Bae et al., 2022) usually employ a memory generator to summarize relevant memories from past sessions, such as user portraits. In this step, the memory generator can either be a separately trained model or a powerful large language model (LLM) like GPT4 (OpenAI, 2023); Subsequently, a dedicated memory database, or a memory bank, is used to store these memories. Some studies (Zhong et al., 2023b) even store past conversational utterances directly in the storage; Going a step further, some works (Bae et al., 2022; Wang et al., 2023) propose the use of specific memory management operations to update and iterate the memory database; The final and indispensable step involves employing a sentence embedding model (Guu et al., 2020; Lewis et al., 2020) to retrieve the most relevant memories from the memory database in relation to the current conversation. The current conversation and related memories are then inputted into a specialized response generator to produce the final response.

To address these concerns, we propose a LLMbased COmpressive Memory-Enhanced Dialogue sYstems framework (COMEDY). COMEDY marks a significant departure from existing methodologies, as it operates without a retrieval module. At its core, COMEDY adopts a groundbreaking “One-for- All” approach, utilizing a single, unified model to manage the entire process from memory generation, compression to final response generation, as shown in Figure 1 (b): It firstly involves distilling session-specific memory from past dialogues, encompassing fine-grained session summaries, including event recaps, and detailed user and bot portraits; In a break from traditional systems, COMEDY eschews the use of a memory database for storing these insights. Instead, it reprocesses and condenses memories from all past interactions, forming a compressive memory. The first part is the concise events that have occurred throughout all the conversations, creating a historical narrative that the system can draw upon. The second and third parts consist of a detailed user profile and the dynamic relationship changes between the user and chatbot across sessions, both derived from past conversational events. This holistic memory allows COMEDY to generate responses that are not only contextually aware but also personalized and adaptive to the evolving nature of the user-chatbot relationship; Finally, COMEDY skillfully integrates this compressive memory into ongoing conversations, enabling contextually memory-enhanced interactions. Unlike retrieval-based systems that may struggle to fetch pertinent memories from a vast database, COMEDY’s compressive memory is inherently designed to prioritize salient information, allowing for quicker and more accurate memory utilization.