Beyond Scale: the Diversity Coefficient as a Data Quality Metric
  Demonstrates LLMs are Pre-trained on Formally Diverse Data
Current trends to pre-train capable Large Language Models (LLMs) mostly focus
on scaling of model and dataset size. However, the 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 ground and understand formal aspects of data
quality, to 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, show
it's high for publicly available LLM datasets, and conjecture it can be used to
build useful diverse datasets for LLMs.
649a3ea6685215f724840d65
Alycia Lee
649a3ea6685215f724840d66
Brando Miranda
admin_assigned
2023-06-27T14:14:04.912Z
60ec6453046c81764c7e06f0
https://cdn-avatars.huggingface.co/v1/production/uploads/60ec6453046c81764c7e06f0/gnKlFE5bkpvbDQNb2jrnV.png
Brando Miranda
user
brando
649a3ea6685215f724840d67
Sanmi Koyejo
admin_assigned
2023-06-27T14:14:30.111Z
64931e7e2da595588288f161
https://cdn-avatars.huggingface.co/v1/production/uploads/64931e7e2da595588288f161/4jOhJOFsU7RVFMgGk5kO7.jpeg
Sanmi Koyejo
user
sanmikoyejo
Beyond Scale: the Diversity Coefficient as a Data Quality Metric
  Demonstrates LLMs are Pre-trained on Formally Diverse Data
Current trends to pre-train capable Large Language Models (LLMs) mostly focus
on scaling of model and dataset size. However, the 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 ground and understand formal aspects of data
quality, to 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, show
it's high for publicly available LLM datasets, and conjecture it can be used to
build useful diverse datasets for LLMs.
Task2Vec diversity coefficient is used to assess and validate data diversity in pre-training datasets for Large Language Models (LLMs).
Large Language Models (LLMs) Task2Vec diversity coefficient latent concepts