CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large
  Language Models in 167 Languages
Authors: name: Thuat Nguyen; name: Chien Van Nguyen; name: Viet Dac Lai; name: Hieu Man; name: Nghia Trung Ngo; name: Franck Dernoncourt; name: Ryan A. Rossi; name: Thien Huu Nguyen
Published: 2023-09-17 23:49:10

Abstract: The driving factors behind the development of large language models (LLMs)
with impressive learning capabilities are their colossal model sizes and
extensive training datasets. Along with the progress in natural language
processing, LLMs have been frequently made accessible to the public to foster
deeper investigation and applications. However, when it comes to training
datasets for these LLMs, especially the recent state-of-the-art models, they
are often not fully disclosed. Creating training data for high-performing LLMs
involves extensive cleaning and deduplication to ensure the necessary level of
quality. The lack of transparency for training data has thus hampered research
on attributing and addressing hallucination and bias issues in LLMs, hindering
replication efforts and further advancements in the community. These challenges
become even more pronounced in multilingual learning scenarios, where the
available multilingual text datasets are often inadequately collected and
cleaned. Consequently, there is a lack of open-source and readily usable
dataset to effectively train LLMs in multiple languages. To overcome this
issue, we present CulturaX, a substantial multilingual dataset with 6.3
trillion tokens in 167 languages, tailored for LLM development. Our dataset
undergoes meticulous cleaning and deduplication through a rigorous pipeline of
multiple stages to accomplish the best quality for model training, including
language identification, URL-based filtering, metric-based cleaning, document
refinement, and data deduplication. CulturaX is fully released to the public in
HuggingFace to facilitate research and advancements in multilingual LLMs:
https://huggingface.co/datasets/uonlp/CulturaX.