Datasets:
license: mit
language:
- en
tags:
- interpretability
- linguistics
pretty_name: CausalGym
size_categories:
- 10K<n<100K
CausalGym is a benchmark for comparing the performance of causal interpretability methods on a variety of simple linguistic tasks taken from the SyntaxGym evaluation set (Gauthier et al., 2020, Hu et al., 2020) and converted into a format suitable for interventional interpretability.
The dataset includes train/dev/test splits (exactly as used in the experiments in the paper).
The base/src columns are the prompts on which intervention is done. Each of these is a list of strings,
with each string being a span in the template which is aligned by index and may have an unequal number
of tokens. The base_label and src_label columns are the ground truth next-token predictions that we
train/evaluate on, and the base_type and src_type columns indicate the class (always binary) of the prompts.
Finally, the task column indicates which task this row is from. You should train separately on each task since
each one studies a different linguistic feature.
Citation
If using this dataset, please cite the CausalGym paper as well as the preceding SyntaxGym papers.
@article{arora-etal-2024-causalgym,
title = "{C}ausal{G}ym: Benchmarking causal interpretability methods on linguistic tasks",
author = "Arora, Aryaman and Jurafsky, Dan and Potts, Christopher",
journal = "arXiv:2402.12560",
year = "2024",
url = "https://arxiv.org/abs/2402.12560"
}
@inproceedings{gauthier-etal-2020-syntaxgym,
title = "{S}yntax{G}ym: An Online Platform for Targeted Evaluation of Language Models",
author = "Gauthier, Jon and Hu, Jennifer and Wilcox, Ethan and Qian, Peng and Levy, Roger",
editor = "Celikyilmaz, Asli and Wen, Tsung-Hsien",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-demos.10",
doi = "10.18653/v1/2020.acl-demos.10",
pages = "70--76",
}
@inproceedings{hu-etal-2020-systematic,
title = "A Systematic Assessment of Syntactic Generalization in Neural Language Models",
author = "Hu, Jennifer and Gauthier, Jon and Qian, Peng and Wilcox, Ethan and Levy, Roger",
editor = "Jurafsky, Dan and Chai, Joyce and Schluter, Natalie and Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.158",
doi = "10.18653/v1/2020.acl-main.158",
pages = "1725--1744",
}