This contains script that reimplements KAPING framework (Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering, Baek et al. 2023 URL)

The process for KAPING will work as follows:

• at least python3.8 (tested on python3.8, python3.10, python 3.11)

• After cloning the repository, install the requirements

$ pip install -r requirements.txt

.
├── README.md
├── arguments.py
├── kaping
│   ├── entity_extractor.py
│   ├── entity_injection.py
│   ├── entity_verbalization.py
│   └── model.py
├── qa
│   ├── qa_evaluate.py
│   ├── qa_inference.py
│   └── qa_preprocessing.py
├── requirements.txt
└── run.py 

• With default, this script will run a QA-inference model using Bert-large-uncased with KAPING (retrieved top-10-triples) to create prompt on CPU

$ python run.py --input <mintaka_dataset.json> 

• To check all arguments

$ python run.py -h

• Pipeline are designed to treat Question and Context together as a Prompt and fed into the pipeline to request model to generate answer, not find the answer from the given inputs. Thus the inference task used are text2text-generation or text-generation depending on which model is compatible.

• Available to test is gpt2, t5-small, t5-base, t5-large, but if you have sufficient space resource, you can try larger t5. Also, if you want to try other models, please check back on https://huggingface.co/.