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The ROOTS Search Tool: Data Transparency for LLMs

Paper · 2023 · Aleksandra Piktus, Christopher Akiki, Paulo Villegas et al.

Summary

Training corpora for large language models are usually massive web scrapes that almost nobody inspects qualitatively. Without inspection it is hard to know which language varieties they cover, whether model outputs are memorized, or whether evaluation benchmarks leaked into the data. The authors tackle this for ROOTS, the 1.6TB multilingual corpus used to pre-train BLOOM. They release a public search engine on Hugging Face Spaces that offers both fuzzy (BM25-ranked) and exact matching over the entire collection. For fuzzy retrieval, documents are cut into pieces of up to 128 words and indexed with Pyserini using Lucene analyzers. Exact search builds context windows around literal matches on demand. To stay within BigScience's data governance commitments, the tool shows only short snippets with traceable IDs, visibly masks personal information, and lets users flag problematic results. Case studies show how it supports checks for memorization, invented facts, benchmark contamination, unlisted languages and preprocessing errors. ROOTS is described as the largest corpus searchable in this manner, making the tool a step toward routine auditing of LLM training data.

Key points

  • ROOTS covers 46 natural and 13 programming languages, organized into 498 datasets. Roughly 598M documents yield about 2.17B snippets, held in 13 BM25 indices with a combined size of around 2.5TB.
  • Each index corresponds to one language, one language group (indic, nigercongo) or code. Chinese tags zh/zht/zhs share a single index.
  • PII is redacted on the backend with an improved version of the BigScience regex script, mainly because OSCAR contains many personal details. Result IDs encode the dataset name, document ID and snippet position.
  • Using the tool, the authors found that XNLI had leaked into the training data, so it is not a valid benchmark for BLOOM.
  • Ukrainian and Danish text appears in ROOTS (e.g., from stackexchange, OSCAR and parsed academic PDFs) even though neither language is officially listed. This is relevant for studying transfer to "unseen" languages.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of CopenhagenCopenhagen · Denmark116 works · 15,292 citations🎓Sapienza University of RomeRome · Italy60 works · 10,105 citations🎓Leipzig UniversityLeipzig · Germany26 works · 8,622 citations🎓Center for Scalable Data Analytics and Artificial IntelligenceDresden · Germany10 works · 77 citations🏢MavenoidStockholm · Sweden2 works · 192 citations🏢Telefonica Research and DevelopmentMadrid · Spain2 works · 192 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,894 works

Cite

Cite as 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. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.acl-demo.29

@inproceedings{piktus2023roots,
  title = {{The ROOTS Search Tool: Data Transparency for LLMs}},
  author = {Aleksandra Piktus, Christopher Akiki, Paulo Villegas, Hugo Laurençon, Gérard Dupont, Sasha Luccioni, Yacine Jernite and Anna Rogers},
  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)},
  year = {2023},
  doi = {10.18653/v1/2023.acl-demo.29},
  url = {https://doi.org/10.18653/v1/2023.acl-demo.29}
}

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