REAR

REAR is a RElevance-Aware Retrieval-augmented framework for open-domain question answering. [paper]

The checkpoint is availible on huggingface🤗. [checkpoint]

🚀 Quick Start

1. Install dependencies.

   pip install -r requirements.txt

2. Run the following codes:

   from rear.src.inf import get_vllm_model
   from rear.src.routing import reliability
   get_vllm_model("RUCAIBox/rear-llama-7b-hf")
   example = {
       "question": "Who won the first Noble Prize in Physics",
       "ctxs": [
           "Wilhelm Conrad Röntgen won first Nobel Prize in Physics.",
           "Wilhelm Conrad Röntgen won it for discovery of X-rays",
           "Albert Einstein was awarded the 1921 Nobel Prize in Physics",
           "The Nobel Prize in Physics is a yearly award.",
           "First law of thermodynamics was stated by William"
           ]
       }
   final_answer = reliability(example)['rely_answer']
   print(final_answer)

🔍 Training and Inference Scripts

1. Training.

   bash train.sh meta-llama/Llama-2-7b-hf [model output path] [training data] [deepspeed config file]

2. Inference.

   First, to generate answers and the path-reliability scores:

   python rear/inference.py \
       --model_path RUCAIBox/rear-llama-7b-hf \
       --phase reliability \
       --source [test file] \
       --outfile [output file(1)]

   Second, to generate the knowledge-consistency scores:

   python rear/inference.py \
       --model_path RUCAIBox/rear-llama-7b-hf \
       --phase consistency \
       --source [output file(1)] \
       --outfile [output file(2)]

   The QA pairs, along with the documents used for inference, are available for download at the following link: [data].

   After running these scripts, if you have provided "reference" as the ground truth in the test source data, the EM (Exact Match) and F1 scores will be automatically calculated.

🌟 Acknowledgement

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

@article{wang2024rear,
    title={REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering},
    author={Wang, Yuhao and Ren, Ruiyang and Li, Junyi and Zhao, Wayne Xin and Liu, Jing and Wen, Ji-Rong},
    journal={arXiv preprint arXiv:2402.17497},
    year={2024}
}