Data Processing for the OpenGPT-X Model Family

• URL: http://arxiv.org/abs/2410.08800v1
• Date: Fri, 11 Oct 2024 13:34:24 GMT
• Title: Data Processing for the OpenGPT-X Model Family
• Authors: Nicolo' Brandizzi, Hammam Abdelwahab, Anirban Bhowmick, Lennard Helmer, Benny Jörg Stein, Pavel Denisov, Qasid Saleem, Michael Fromm, Mehdi Ali, Richard Rutmann, Farzad Naderi, Mohamad Saif Agy, Alexander Schwirjow, Fabian Küch, Luzian Hahn, Malte Ostendorff, Pedro Ortiz Suarez, Georg Rehm, Dennis Wegener, Nicolas Flores-Herr, Joachim Köhler, Johannes Leveling,
• Abstract summary: This paper presents a comprehensive overview of the data preparation pipeline developed for the OpenGPT-X project. The project goal is to create open and high-performance multilingual large language models (LLMs) We explain all data processing steps, starting with the data selection and requirement definition to the preparation of the final datasets for model training.
• Score: 32.8178473342263
• License: http://creativecommons.org/licenses/by/4.0/
• 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 datasets for model training. 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.

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