The ROOTS Search Tool: Data Transparency for LLMs

Aleksandra Piktus, Christopher Akiki, Paulo Villegas, Hugo Laurençon, Gérard Dupont, Sasha Luccioni, Yacine Jernite, Anna Rogers

Abstract
ROOTS is a 1.6TB multilingual text corpus developed for the training of BLOOM, currently the largest language model explicitly accompanied by commensurate data governance efforts. In continuation of these efforts, we present the ROOTS Search Tool: a search engine over the entire ROOTS corpus offering both fuzzy and exact search capabilities. ROOTS is the largest corpus to date that can be investigated this way. The ROOTS Search Tool is open-sourced and available on Hugging Face Spaces: https://huggingface.co/spaces/bigscience-data/roots-search. We describe our implementation and the possible use cases of our tool.
Anthology ID:
2023.acl-demo.29
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Danushka Bollegala, Ruihong Huang, Alan Ritter
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
304–314
Language:
URL:
https://aclanthology.org/2023.acl-demo.29
DOI:
10.18653/v1/2023.acl-demo.29
Bibkey:
Cite (ACL):
Aleksandra Piktus, Christopher Akiki, Paulo Villegas, Hugo Laurençon, Gérard Dupont, Sasha Luccioni, Yacine Jernite, and Anna Rogers. 2023. The ROOTS Search Tool: Data Transparency for LLMs. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 304–314, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
The ROOTS Search Tool: Data Transparency for LLMs (Piktus et al., ACL 2023)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2023.acl-demo.29.pdf
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