GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks

Code of our The Web Conference 2024 paper GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks

Author: Mengmei Zhang, Mingwei Sun, Peng Wang, Shen Fan, Yanhu Mo, Xiaoxiao Xu, Hong Liu, Cheng Yang, Chuan Shi

Model Pipeline

• Pre-training Graph Model Phase. In the pre-training phase, we employ link prediction as the self-supervised task for pre-training the graph model.

• Producer Phase. In the Producer phase, we employ LLM to summary Node/Neighbor Information.

• Translator Training Phase.

  ​ Stage 1: Training the Translator for GraphModel-Text alignment.

  ​ Stage 2: Training the Translator for GraphModel-LLM alignment.

• Translator Generate Phase. Generate the predictions with the pre-trained Translator model.

Installation

We run our experiment with the following settings.

• CPU: Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz
• GPU: Tesla V100-SXM2-32GB
• OS: Linux (Ubuntu 18.04.6 LTS)
• Python==3.9, CUDA==11.4, Pytorch==1.12.1

The ./requirements.txt list all Python libraries that GraphTranslator depend on, and you can install using:

conda create -n graphtranslator python=3.9
conda activate graphtranslator
git clone https://github.com/alibaba/GraphTranslator.git
cd GraphTranslator/
pip install -r requirements.txt

Datasets & Models

Download datasets and model checkpoints used in this project with huggingface.

ArXiv Dataset

Download files bert_node_embeddings.pt, graphsage_node_embeddings.pt and titleabs.tsv from link and insert them to ./data/arxiv.

cd ./data/arxiv
git lfs install
git clone [email protected]:datasets/Hualouz/GraphTranslator-arxiv

Translator Model

Download bert-base-uncased.zip from link and unzip it to ./Translator/models.

cd Translator/models/
git lfs install
git clone [email protected]:Hualouz/Qformer
unzip bert-base-uncased.zip

ChatGLM2-6B Model

Download the ChatGLM2-6B model from link and insert it to ./Translator/models

cd ./Translator/models
git lfs install
git clone [email protected]:THUDM/chatglm2-6b

Run

Producer Phase

• Generate node summary text with LLM (ChatGLM2-6B).

cd ./Producer/inference
python producer.py

Training Phase

Train the Translator model with the prepared ArXiv dataset.

• Stage 1 Training

Train the Translator for GraphModel-Text alignment. The training configurations are in the file ./Translator/train/pretrain_arxiv_stage1.yaml.

cd ./Translator/train
python train.py --cfg-path ./pretrain_arxiv_stage1.yaml

After stage 1, you will get a model checkpoint stored in ./Translator/model_output/pretrain_arxiv_stage1/checkpoint_0.pth.

• Stage 2 Training

Train the Translator for GraphModel-LLM alignment. The training configurations are in the file ./Translator/train/pretrain_arxiv_stage2.yaml.

cd ./Translator/train
python train.py --cfg-path ./pretrain_arxiv_stage2.yaml

After stage 2, you will get a model checkpoint stored in ./Translator/model_output/pretrain_arxiv_stage2/checkpoint_0.pth.

After all the training stages , you will get a model checkpoint that can translate GraphModel information into that the LLM can understand.

• Note: Training phase is not necessary if you only want to obtain inference results with our pre-trained model checkpoint. You can download our pre-trained checkpoint checkpoint_0.pth from link and place it in the ./Translator/model_output/pretrain_arxiv_stage2 directory. Then skip the whole Training Phase and go to the Generate Phase.

Generate Phase

• generate prediction with the pre-trained Translator model. The generate configurations are in the file ./Translator/train/pretrain_arxiv_generate_stage2.yaml. As to the inference efficiency, it may take a while to generate all the predictions and save them into file.

cd ./Translator/train
python generate.py

The generated prediction results will be saved in ./data/arxiv/pred.txt.

Evaluation

Evaluate the accuracy of the generated predictions.

cd ./Evaluate
python eval.py

Citation

@inproceedings{zhang2024graphtranslator,
  title={GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks},
  author={Zhang, Mengmei and Sun, Mingwei and Wang, Peng and Fan, Shen and Mo, Yanhu and Xu, Xiaoxiao and Liu, Hong and Yang, Cheng and Shi, Chuan},
  booktitle={Proceedings of the ACM on Web Conference 2024},
  pages={1003--1014},
  year={2024}
}

Acknowledgements

Thanks to all the previous works that we used and that inspired us.

• LAVIS: The logical architecture of LAVIS library served as the foundation for our code development.
• ChatGLM: An open-source LLM with the amazing language capabilities.
• BLIP2: our model is inspired from BLIP2.