Aleph-Alpha-GermanWeb: Improving German-language LLM pre-training with model-based data curation and synthetic data generation

1. 1Combine heuristic and model-based filtering techniques to curate high-quality data
2. 2Generate synthetic data to augment the dataset and improve model performance
3. 3Apply the curation pipeline to create a large-scale pre-training dataset
4. 4Evaluate the effectiveness of the dataset in improving LLM performance and training efficiency

NLP engineers and researchers on a team can benefit from this approach to improve the performance and training efficiency of German-language LLMs, and data scientists can apply these techniques to other languages and domains

💡 Data quality can significantly boost LLM performance and training efficiency, and model-based data curation and synthetic data generation can be effective techniques for improving data quality

🚀 Improve German-language LLMs with model-based data curation & synthetic data generation!