Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models
Alex Havrilla, Andrew Dai, Laura O'Mahony, Koen Oostermeijer, Vera Zisler, Alon Albalak, Fabrizio Milo, Sharath Chandra Raparthy, Kanishk Gandhi, Baber Abbasi, Duy Phung, Maia Iyer, Dakota Mahan, Chase Blagden, Srishti Gureja, Mohammed Hamdy, Wen-Ding Li, Giovanni Paolini, Pawan Sasanka Ammanamanchi, Elliot Meyerson
2024-12-05
Summary
This paper discusses the evaluation of synthetic data generated by Large Language Models (LLMs) based on three key characteristics: quality, diversity, and complexity.
What's the problem?
Currently, there are not many ways to compare different methods of generating synthetic data using LLMs. This makes it hard to understand which methods work best and where improvements are needed. Additionally, existing benchmarks often focus on simple tasks and do not consider the importance of having varied and complex data, which can limit the effectiveness of AI models trained on this data.
What's the solution?
To address these issues, the researchers propose a new way to evaluate synthetic data generation algorithms by focusing on three important aspects: quality (how accurate the data is), diversity (how varied the data is), and complexity (how detailed or intricate the data is). They find that high-quality data is crucial for models that work well with familiar tasks, while diverse data helps models perform better in unfamiliar situations. The study also highlights trade-offs between these characteristics, suggesting that improving one may affect another. By analyzing different components of the synthetic data generation process, they can better understand how to improve these algorithms.
Why it matters?
This research is significant because it helps improve how AI systems are trained by ensuring they have access to high-quality, diverse, and complex synthetic data. By balancing these characteristics, developers can create more effective AI models that perform better across a wider range of tasks. This could lead to advancements in various fields such as natural language processing, computer vision, and machine learning applications.
Abstract
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.