metadata
datasets:
- nilq/babylm-10M
language:
- en
- GPT-2 model submitted by team CLAUSE Bielefeld to the BabyLM challenge 2023
- implements a very naive curriculum learning approach inspired by usage-based linguistics: training examples are ordered according to complexity measures from research on child-directed speech (please consult paper for more info)
Citation:
@inproceedings{bunzeck-zarriess-2023-gpt,
title = "{GPT}-wee: How Small Can a Small Language Model Really Get?",
author = "Bunzeck, Bastian and
Zarrie{\ss}, Sina",
editor = "Warstadt, Alex and
Mueller, Aaron and
Choshen, Leshem and
Wilcox, Ethan and
Zhuang, Chengxu and
Ciro, Juan and
Mosquera, Rafael and
Paranjabe, Bhargavi and
Williams, Adina and
Linzen, Tal and
Cotterell, Ryan",
booktitle = "Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.conll-babylm.2",
doi = "10.18653/v1/2023.conll-babylm.2",
pages = "35--46",
}