Hello Again! LLM-powered Personalized Agent for Long-term Dialogue

Hao Li^1*, Chenghao Yang^2*, An Zhang^3†, Yang Deng³, Xiang Wang², Tat-Seng Chua³,

¹University of Electronic Science and Technology of China

²University of Science and Technology of China

³National University of Singapore

The official implementation of paper "Hello Again! LLM-powered Personalized Agent for Long-term Dialogue".

⚙️ Preparations

Environment Requirements

We recommend the following dependencies:

• Python 3.10.0
• PyTorch 1.13.0
• Transformers (>= 4.32.0)

Then, please install other environment dependencies through:

pip install -r requirements.txt

The recommended GPU memory is more than 32 GB.

Dataset Preparation

The datasets for event summary, persona extraction, response generation and MSC can be downloaded here. Please organize the dataset path as LD-Agent/dataset.

Metric Preparation

To automatically evaluate response quality, you should download the compressed metric files here. Then decompress it and organize it to LD-Agent/nlgeval/metric.

💡 Quick Start

Training

We refer to the training approach of ChatGLM3-6B and separately provide LoRA tuning strategy for event summary, persona extraction, and response generation. You can run the following instructions to train these modules.

Summarizer

bash scripts/summarizer_tuning.sh

Extractor

bash scripts/extractor_tuning.sh

Generator

bash scripts/generator_tuning.sh

You can adjust the detailed training configs in Trainer/configs.

Evaluation

We provide the evaluation implementations on both ChatGPT and ChatGLM3-6B.

ChatGPT

To evaluate using ChatGPT, you can edit the ${API_KEY} in scripts/msc_gpt_eval.sh to your openai API key and run:

bash scripts/msc_gpt_eval.sh

ChatGLM

To evaluate using ChatGLM3-6B, you can run:

bash scripts/msc_glm_eval.sh

Edit the ${SUMMARIZER}, ${EXTRACTOR}, and ${GENERATOR} to specify the LoRA models used for event summary, persona extraction, and response generation, respectively. The setting of "default" indicates employing original ChatGLM to the target module.

Reference

If you found this code useful, please cite the following paper:

@article{LD-Agent,
  title={Hello Again! LLM-powered Personalized Agent for Long-term Dialogue},
  author={Li, Hao and 
          Yang, Chenghao and 
          Zhang, An and 
          Deng, Yang and 
          Wang, Xiang and 
          Chua, Tat-Seng},
  journal={arXiv preprint arXiv:2406.05925},
  year={2024}
}