COMEDY_13B_DPO

COMEDY: COmpressive Memory-Enhanced Dialogue sYstems framework.

Github: https://github.com/nuochenpku/COMEDY

Paper: https://arxiv.org/abs/2402.11975.pdf

<br> <div align="center"> <img src="comedy.png" width="40%" title="Introduction Figure"> </div>

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.

• COMEDY 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.

• 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.

<br> <div align="center"> <img src="training_strategy.png" width="90%" title="Introduction Figure"> </div>