Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection

Suchin Gururangan, Dallas Card, Sarah Dreier, Emily Gade, Leroy Wang, Zeyu Wang, Luke Zettlemoyer, Noah A. Smith


Abstract
Language models increasingly rely on massive web crawls for diverse text data. However, these sources are rife with undesirable content. As such, resources like Wikipedia, books, and news often serve as anchors for automatically selecting web text most suitable for language modeling, a process typically referred to as quality filtering. Using a new dataset of U.S. high school newspaper articles—written by students from across the country—we investigate whose language is preferred by the quality filter used for GPT-3. We find that newspapers from larger schools, located in wealthier, educated, and urban zones (ZIP codes) are more likely to be classified as high quality. We also show that this quality measurement is unaligned with other sensible metrics, such as factuality or literary acclaim. We argue that privileging any corpus as high quality entails a language ideology, and more care is needed to construct training corpora for language models, with better transparency and justification for the inclusion or exclusion of various texts.
Anthology ID:
2022.emnlp-main.165
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2562–2580
Language:
URL:
https://aclanthology.org/2022.emnlp-main.165
DOI:
Bibkey:
Cite (ACL):
Suchin Gururangan, Dallas Card, Sarah Dreier, Emily Gade, Leroy Wang, Zeyu Wang, Luke Zettlemoyer, and Noah A. Smith. 2022. Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2562–2580, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection (Gururangan et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.165.pdf