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

Anonymous

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, COMEDYadopts 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 integrates 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.

Paper Type: long

Research Area: Question Answering

Contribution Types: NLP engineering experiment, Reproduction study, Publicly available software and/or pre-trained models, Data resources

Languages Studied: English, Chinese