Quantifying the Importance of Data Alignment in Downstream Model Performance

The study emphasizes data alignment's critical role in Large Language Model (LLM) performance, challenging the focus on dataset size. It introduces a Task2Vec-based alignment coefficient, finding a strong negative correlation between alignment and model loss/perplexity on downstream tasks. Key findings include improved model performance with fine-tuning on highly aligned datasets, suggesting a need to reassess LLM training methods. The research highlights the importance of data quality and diversity in training, advocating for strategic resource allocation over quantity.