LLM-Knowledge-Boundary

See our paper: Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation.

🚀 Quick Start

1. Preprocess data and install dependencies.

   bash preparation.sh
   python data_preparation.py -d [nq/tq/hq]

2. Get supporting documents generated by ChatGPT (take Natural Questions dataset as an example).

   OPENAI_API_KEY=[your api key] \
   python run_llm.py \
       --source=data/source/nq.json \
       --usechat \
       --type=generate \
       --ra=none \
       --outfile=data/source/nq-chat.json

🔍 Conduct Experiments

1. Question answering.

   OPENAI_API_KEY=[your api key] \
   python run_llm.py \
       --source=data/source/nq-chat.json \
       --usechat \
       --type=qa \
       --ra=none \
       --outfile=data/qa/nq-none-qa.json

2. Priori judgement.

   OPENAI_API_KEY=[your api key] \
   python run_llm.py \
       --source=data/source/nq-chat.json \
       --usechat \
       --type=prior \
       --ra=dense \
       --outfile=data/prior/nq-dense-prior.json

3. Posteriori judgement.

   OPENAI_API_KEY=[your api key] \
   python run_llm.py \
       --source=data/qa/nq-none-qa.json \
       --usechat \
       --type=post \
       --ra=sparse \
       --outfile=data/post/nq-sparse-post.json

🌟 Acknowledgement

Please cite the following paper if you find our code helpful.

@article{ren2023investigating,
  title={Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation},
  author={Ren, Ruiyang and Wang, Yuhao and Qu, Yingqi and Zhao, Wayne Xin and Liu, Jing and Tian, Hao and Wu, Hua and Wen, Ji-Rong and Wang, Haifeng},
  journal={arXiv preprint arXiv:2307.11019},
  year={2023}
}