COMEDY: COmpressive Memory-Enhanced Dialogue sYstems framework.
Github: https://github.com/nuochenpku/COMEDY
Paper: https://arxiv.org/abs/2402.11975.pdf
Task: Long-Term Conversation Dialogue Generation
Different from previous retrieval-based methods, COMEDY doesn't rely on any retrieval module or database.
Instead, 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 for long-term memory dialogue generation.
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COMEDY firstly involves distilling session-specific memory from past dialogues, encompassing fine-grained session summaries, including event recaps, and detailed user and bot portraits;
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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.
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Finally, COMEDY skillfully integrates this compressive memory into ongoing conversations, enabling contextually memory-enhanced interactions.
Training Dataset
Dolphin, the biggest Chinese long-term conversation dataset, from actual online user-chatbot interactions.
This dataset contains three tasks:
Session-Level Memory Summarization;
Memory Compression;
Memory-Grounded Response Generation,
comprising an extensive collection of 100k samples.
Dolphin is available at Dolphin
Training Strategy
Our training strategies include two stages: Mixed-task training and DPO Alignment.