Beyond Scale: the Diversity Coefficient as a Data Quality Metric Demonstrates LLMs are Pre-trained on Formally Diverse Data

Alycia Lee, Brando Miranda, Sanmi Koyejo

Keywords: machine learning, large language models, metrics, data diversity, data, data quality

Abstract:

Current trends to pre-train capable Large Language Models (LLMs) mostly focus on scaling of model and dataset size.
However, the $\textit{quality}$ of pre-training data is an important factor for training powerful LLMs, yet it is a nebulous concept that has not been fully characterized.
Therefore, we use the recently proposed Task2Vec diversity coefficient to understand formal aspects of data quality that go beyond scale alone.
Specifically, we measure the diversity coefficient of publicly available pre-training datasets to demonstrate that their formal diversity is high when compared to theoretical lower and upper bounds.
In addition, to build confidence in the diversity coefficient, we conduct interpretability experiments and find that the coefficient aligns with intuitive properties of diversity,
e.g., it increases as the number of latent concepts increases.
We conclude the diversity coefficient is reliable and conjecture it can be used to build useful diverse datasets for LLMs.

Submission Number: 42