DCAD-2000: A Multilingual Dataset Across 2000+ Languages With Data Cleaning as Anomaly Detection

MCML Authors

Wen Lai

→ Group Alexander Fraser
 Data Analytics & Statistics

Alexander Fraser

Prof. Dr.

Principal Investigator

Data Analytics & Statistics

Abstract

The rapid development of multilingual large language models (LLMs) highlights the need for high-quality, diverse, and well-curated multilingual datasets. In this paper, we introduce DCAD-2000 (Data Cleaning as Anomaly Detection), a large-scale multilingual corpus constructed from newly extracted Common Crawl data and existing multilingual sources. DCAD-2000 covers 2,282 languages, 46.72TB of text, and 8.63 billion documents, spanning 155 high- and medium-resource languages and 159 writing scripts. To overcome the limitations of existing data cleaning approaches, which rely on manually designed heuristic thresholds, we reframe data cleaning as an anomaly detection problem. This dynamic filtering paradigm substantially improves data quality by automatically identifying and removing noisy or anomalous content. By fine-tuning LLMs on DCAD-2000, we demonstrate notable improvements in data quality, robustness of the cleaning pipeline, and downstream performance, particularly for low-resource languages across multiple multilingual benchmarks.

inproceedings SLW+25

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NeurIPS 2025

39th Conference on Neural Information Processing Systems. San Diego, CA, USA, Nov 30-Dec 07, 2025.

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Authors

Y. Shen • W. Lai • S. Wang • X. Zhang • K. Luo • A. Fraser • M. Sun

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Links

URL

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Research Area

 B2 | Natural Language Processing