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The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset

NeurIPS 2022 · Large language models › Language, safety and interpretability · approach: Multilingual LLM data and culture · 61 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Apply language-specific quality filters to OSCAR using thresholds selected by native speakersOSCAR · 13 OSCAR languagesdocuments removed: 5.2 to 48.8Mixed
Use SimHash with 6-grams and Hamming distance threshold 4 to identify near duplicatesOSCAR · OSCAR documentsdocuments identified as near duplicates: 0.7% (0.07% \sim 2.7%)Helped
Avoid discarding SimHash clusters when documents exceed 6000 charactersOSCAR · Long OSCAR documentsfalse-positive protectionHelped
Add suffix-array substring deduplication for documents longer than 6000 charactersOSCAR · OSCAR documents longer than 6000 charactersduplicate data by bytes: 21.67% (10.61% \sim 32.30%)Helped
Apply MinHash and LSH near-duplicate detection after exact-match deduplicationGitHub code · GitHub codenear-duplicate share: almost 32%Helped
Add URL deduplication before repeated-line template removalPseudo-crawl processing pipeline · Pseudo-crawled websitespreserved dataset size: 7Mb -> 134Mb (x18)Helped

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