title: Surveying the Effects of Quality, Diversity, and Complexity in Synthetic
    Data From Large Language Models
summary: 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.
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paper_title: Surveying the Effects of Quality, Diversity, and Complexity in Synthetic
    Data From Large Language Models
paper_summary: 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.