This is our PyTorch implementation for Bridging the Gap between Knowledge Graphs and LLMs for Multi-hop Question Answering accepted in CIKM 2025.
The code has been tested running under Python 3.10.16 on Linux.
The required packages are as follows:
datasets==3.6.0
peft==0.15.2
torch==2.7.0
tqdm==4.67.1
transformers==4.51.3
wandb==0.19.11
modelscope==1.26.0
cd ./llm
python llm_downloader.py
cd ./encoder
python main.py --data MovieQA --model TransE --max_steps 5000 --batch_size 512
python main.py --data MovieQA --num_epoches 30 --lora_rank 64 --batch_size 8 --train_encoder_model TransE --train_encoder_steps 5000
Data Preprocess is in ./preprocess, please read ./preprocess/README.md to continue.
@inproceedings{luo2025brikqa,
title={Bridging the Gap between Knowledge Graphs and LLMs for Multi-hop Question Answering},
author={Luo, Shijie and Lu, Xinyuan and Zhao, Qinpei and Rao, Weixiong},
booktitle={Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM '25)},
publisher={Association for Computing Machinery},
address={Seoul, Republic of Korea},
pages={5006--5010},
year={2025}
}