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Split the dataset into train/dev/test.jsonl subsets. Also build a train.counterfactual.jsonl subset by perturbing the answers in the train.jsonl subset. Organize all the subsets in the folder data/DATASET.
2. Obtain the rationales
./scripts/run_contrastive_decoding.sh
The generated rationales would be stored at outputs/DATASET.
Counterfactual Training
./scripts/run_counterfactual_training.sh
After training, the checkpoints and the evaluation result will be stored at checkpoints/DATASET.
Citation
@inproceedings{wang-etal-2023-scott,
title = "{SCOTT}: Self-Consistent Chain-of-Thought Distillation",
author = "Wang, Peifeng and
Wang, Zhengyang and
Li, Zheng and
Gao, Yifan and
Yin, Bing and
Ren, Xiang",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.304",
doi = "10.18653/v1/2023.acl-long.304",
pages = "5546--5558",
}