Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models (2412.02980v2)

Abstract: Synthetic data generation with LLMs 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.

Synthetic Data from LLMs: Quality, Diversity, and Complexity

The paper "Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From LLMs" provides a comprehensive exploration into the characteristics that make synthetic data generated by LLMs effective for improving downstream model performance. This survey attempts to unravel the complex interplay between the quality, diversity, and complexity of synthetic datasets, emphasizing how these factors affect model generalization. It critically analyzes current practices in synthetic data generation and identifies gaps that, when bridged, could significantly enhance the capabilities of LLMs through iterative self-improvement.

Key Concepts and Measures

The central theme revolves around three core characteristics of synthetic data—quality, diversity, and complexity (QDC). Each characteristic is meticulously defined and operationalized through a variety of measures:

• Quality: Defined as the "correctness" or "alignment" of data to a desired target distribution, quality measures typically rely on comparisons to human judgment or ground truth reference points. This section discusses various methodologies such as reward models, outcome-based assessments, and process-based evaluations. While neural measures like reward models offer the ability to generalize quality assessment, ground truth measures stand out for their robustness across certain domains like math and coding.
• Diversity: This is the measure of "self-similarity" within data, aiming to capture the breadth and coverage over the sample space. Notable metrics include lexical overlap, attribute-based diversity such as task variety, and embedding-based methods that leverage vector spaces to gauge variety. The survey points out the necessity for both quality and diversity to ensure both in-distribution performance and robust out-of-distribution generalization.
• Complexity: Often underexplored relative to quality and diversity, complexity is conceptualized as the "difficulty" or "compositionality" of data. Measures range from simple token length evaluations to more nuanced psychometric methods and tree structures. Complexity is posited as a critical factor that benefits both in-distribution and OOD generalization.

Effects on Model Generalization

A pivotal section of the paper is the analysis of how each of these dataset characteristics impacts the model's ability to generalize:

• Quality improves in-distribution generalization, but its effects on OOD generalization are more limited unless affiliated with diversity.
• Diversity is shown to be vital for OOD generalization, emphasizing the necessity for synthetic datasets to encompass a wide range of domains and tasks.
• Complexity is essential for both generalization paradigms, though only to a certain optimal level beyond which it might hamper learning.

Methods for Promoting QDC in Synthetic Data

The survey also explores methodologies for promoting these characteristics within synthetic data generation pipelines. Standard approaches employ SOTA LLMs for high-quality generation, but the inclusion of novel techniques for diversity—such as diverse prompting strategies and the use of QD-inspired search algorithms—enriches the dataset. Moreover, complexity is sometimes intentionally evolved through iterative rounds of solution refinement.

Recursive Self-Improvement

One of the paper's salient points is the identification of trade-offs between these characteristics, especially between quality and diversity in model outputs, and the implications for recursive self-improvement. It argues for a balanced approach to synthetic data generation, integrating QDC considerations at all stages—from initial generation to the final distillation into a student model. The iterative nature of self-improvement cycles could see major enhancements by acknowledging and optimizing for these trade-offs.

Implications and Future Directions

The findings and discussions in the survey carry significant implications for the future of AI model development. For synthetic data to drive LLMs towards human-like or exceed current generalization capabilities, algorithms need to be explicitly QDC-aware. The paper closes with a call for:

• More sophisticated benchmarks that consider all components of QDC in both data and model outputs.
• Deeper investigation into the synergistic effects of QDC components within synthetic data generation.
• The optimization of model architectures to leverage increasingly complex synthetic data efficiently.

This paper lays a critical groundwork, signaling the potential and challenges for synthetic datasets in LLM development. As AI systems drive into domains demanding consistent self-improvement and adaptation, understanding and manipulating these QDC dynamics will prove indispensable. This survey adeptly frames these considerations, outlining a research trajectory ripe with opportunity.