Data Processing for the OpenGPT-X Model Family

TL;DR

This paper details the data processing pipeline for the OpenGPT-X project, focusing on multilingual data preparation, filtering, and compliance to develop high-quality European language models.

Contribution

It introduces specialized data processing pipelines for curated and web data, enhancing transparency and regulatory compliance in multilingual LLM training.

Findings

Effective filtering and deduplication methods for web data

Enhanced transparency in dataset preparation

Insights into multilingual data challenges

Abstract

This paper presents a comprehensive overview of the data preparation pipeline developed for the OpenGPT-X project, a large-scale initiative aimed at creating open and high-performance multilingual large language models (LLMs). The project goal is to deliver models that cover all major European languages, with a particular focus on real-world applications within the European Union. We explain all data processing steps, starting with the data selection and requirement definition to the preparation of the final filtered data. We distinguish between curated data and web data, as each of these categories is handled by distinct pipelines, with curated data undergoing minimal filtering and web data requiring extensive filtering and deduplication. This distinction guided the development of specialized algorithmic solutions for both pipelines. In addition to describing the processing methodologies, we provide an in-depth analysis of the datasets, increasing transparency and alignment with European data regulations. Finally, we share key insights and challenges faced during the project, offering recommendations for future endeavors in large-scale multilingual data preparation for LLMs.