Optimizing Pretraining Datasets for Large Language Models Through Recursive Perplexity Correlations

Abstract The exponential expansion of textual data has catalyzed the development of sophisticated language models, enabling unprecedented levels of language comprehension and generation. Introducing perplexity correlations as a mechanism for optimizing pretraining datasets constitutes a groundbreaking and substantial advancement, significantly enhancing the quality and effectiveness of the data employed in language model training. Through the careful analysis of perplexity scores in relation to various dataset attributes, this novel approach facilitates the identification and refinement of high-quality data segments, thereby constructing a more coherent and representative training corpus. The application of this methodology to the Mistral Large Language Model resulted in notable reductions in perplexity scores and marked improvements in performance across standard benchmarking evaluations. These enhancements demonstrate the critical importance of data quality in the training of language models and demonstrate that strategic dataset optimization via perplexity correlations can substantially elevate model performance and reliability. Consequently, this study provides a robust framework for ongoing advancements in language model development, emphasizing the significant role of data refinement in achieving superior natural language processing capabilities.