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

• URL: http://arxiv.org/abs/2402.11975v2
• Date: Mon, 1 Jul 2024 09:38:06 GMT
• Title: Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations
• Authors: Nuo Chen, Hongguang Li, Juhua Huang, Baoyuan Wang, Jia Li,
• Abstract summary: This study introduces a novel framework, COmpressive Memory-Enhanced Dialogue sYstems (COMEDY), which eschews traditional retrieval modules and memory databases. Central to COMEDY is the concept of compressive memory, which intergrates session-specific summaries, user-bot dynamics, and past events into a concise memory format.
• Score: 39.05338079159942
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing retrieval-based methods have made significant strides in maintaining long-term conversations. However, these approaches face challenges in memory database management and accurate memory retrieval, hindering their efficacy in dynamic, real-world interactions. This study introduces a novel framework, COmpressive Memory-Enhanced Dialogue sYstems (COMEDY), which eschews traditional retrieval modules and memory databases. Instead, COMEDY adopts a "One-for-All" approach, utilizing a single language model to manage memory generation, compression, and response generation. Central to this framework is the concept of compressive memory, which intergrates session-specific summaries, user-bot dynamics, and past events into a concise memory format. To support COMEDY, we curated a large-scale Chinese instruction-tuning dataset, Dolphin, derived from real user-chatbot interactions. Comparative evaluations demonstrate COMEDY's superiority over traditional retrieval-based methods in producing more nuanced and human-like conversational experiences. Our codes are available at https://github.com/nuochenpku/COMEDY.

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