Surveying the Effects of Quality, Diversity, and Complexity in Synthetic
  Data From Large Language Models
Authors: name: Alex Havrilla, name: Andrew Dai, name: Laura O'Mahony, name: Koen Oostermeijer, name: Vera Zisler, name: Alon Albalak, name: Fabrizio Milo, name: Sharath Chandra Raparthy, name: Kanishk Gandhi, name: Baber Abbasi, name: Duy Phung, name: Maia Iyer, name: Dakota Mahan, name: Chase Blagden, name: Srishti Gureja, name: Mohammed Hamdy, name: Wen-Ding Li, name: Giovanni Paolini, name: Pawan Sasanka Ammanamanchi, name: Elliot Meyerson

Synthetic data generation with Large Language Models is a promising paradigm
for augmenting natural data over a nearly infinite range of tasks. Given this
variety, direct comparisons among synthetic data generation algorithms are
scarce, making it difficult to understand where improvement comes from and what
bottlenecks exist. We propose to evaluate algorithms via the makeup of
synthetic data generated by each algorithm in terms of data quality, diversity,
and complexity. We choose these three characteristics for their significance in
open-ended processes and the impact each has on the capabilities of downstream
models. We find quality to be essential for in-distribution model
generalization, diversity to be essential for out-of-distribution
generalization, and complexity to be beneficial for both. Further, we emphasize
the existence of Quality-Diversity trade-offs in training data and the
downstream effects on model performance. We then examine the effect of various
components in the synthetic data pipeline on each data characteristic. This
examination allows us to taxonomize and compare synthetic data generation
algorithms through the components they utilize and the resulting effects on
data QDC composition. This analysis extends into a discussion on the importance
of balancing QDC in synthetic data for efficient reinforcement learning and
self-improvement algorithms. Analogous to the QD trade-offs in training data,
often there exist trade-offs between model output quality and output diversity
which impact the composition of synthetic data. We observe that many models are
currently evaluated and optimized only for output quality, thereby limiting
output diversity and the potential for self-improvement. We argue that
balancing these trade-offs is essential to the development of future
self-improvement algorithms and highlight a number of works making progress in
this direction.