The relationship between data and evaluation is pivotal for enhancing model capabilities, especially in large language model (LLM) pre-training.
Data plays a prospective role in shaping model capabilities, while evaluation serves to reveal these capabilities retrospectively.
The insights presented in the recent ArXiv publication underscore the importance of this closed-loop system in optimizing model performance.