---
license: apache-2.0
datasets:
- Nuo97/Dolphin-DPO
language:
- zh
metrics:
- bleu
pipeline_tag: question-answering
---

# COMEDY: COmpressive Memory-Enhanced Dialogue sYstems framework.

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

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


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<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**](https://huggingface.co/datasets/Nuo97/Dolphin-DPO)

### Training Strategy

Our training strategies include two stages: Mixed-task training and DPO Alignment.

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<div align="center">
  <img src="training_strategy.png" width="100%" title="Introduction Figure">
</div>