KB-BINDER

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"Few-shot In-context Learning for Knowledge Base Question Answering" [ACL2023]

// repository documentation

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# KB-BINDER
The implementation for ACL2023 paper [Few-shot In-context Learning for Knowledge Base Question Answering](http://arxiv.org/abs/2305.01750)
<img width="1237" alt="KBQA-BINDER" src="https://github.com/ltl3A87/KB-BINDER/assets/55973524/d9ceefbe-392e-4749-bf1f-58a93e97b254">

## Set up

1. Set up the knowledge base server: Follow [Freebase Setup](https://github.com/dki-lab/Freebase-Setup) to set up a Virtuoso triplestore service. After starting your virtuoso service, replace the url in `sparql_executer.py` with your own.
2. Download GrailQA/WebQSP/GraphQA/MetaQA and other required files from the [link](https://drive.google.com/drive/folders/1g8ZpMLSw95KwjisXEw07rVVC3TJ1LZdn?usp=sharing) and put them under `data/`.
3. Install all required libraries:
```
$ pip install -r requirements.txt
```
You can download the index file and put it under `contriever_fb_relation
/freebase_contriever_index/` with this [link](https://drive.google.com/file/d/1hnyW-_k0YaAUZDTdYzhbKDTnFuLEW-W2/view?usp=sharing).

## Run Experiments

### KB-BINDER:
```
$ python3 few_shot_kbqa.py --shot_num 40 --temperature 0.3 \
 --api_key [your api key] --engine [engine model name] \
 --train_data_path [your train data path] --eva_data_path [your eva data path] \
 --fb_roles_path [your freebase roles file path] --surface_map_path [your surface map file path]
```

### KB-BINDER-R:
```
$ python3 few_shot_kbqa.py --shot_num 40 --temperature 0.3 \
 --api_key [your api key] --engine [engine model name] --retrieval \
 --train_data_path [your train data path] --eva_data_path [your eva data path] \
 --fb_roles_path [your freebase roles file path] --surface_map_path [your surface map file path]
```

As the codex API has been closed, you may use other engine.

KB-BINDER

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