Compress to Impress: Unleashing the Potential of Compressive Memory in
  Real-World Long-Term Conversations
Nuo Chen Hongguang Li Juhua Huang Baoyuan Wang Jia Li
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.