调查大型语言模型合成数据中质量、多样性和复杂性的影响
Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models
AI总结：
本文从质量、多样性和复杂性（QDC）构成评估 LLM 合成数据生成算法，揭示三者对分布内外泛化的作用及质量—多样性权衡，并据此分类算法、指导自我改进。
摘要：
使用大型语言模型生成合成数据是一种很有前景的范式，可在几乎无限范围的任务上扩充自然数据。鉴于这种多样性，合成数据生成算法之间的直接比较仍然稀缺，这使得人们难以理解改进来自何处以及存在哪些瓶颈。我们建议根据每种算法所生成合成数据的构成，从数据质量、多样性和复杂性方面评估算法。我们选择这三个特征，是因为它们在开放式过程中具有重要意义，并且各自都会影响下游模型的能力。我们发现，质量对于分布内模型泛化至关重要，多样性对于分布外泛化至关重要，而复杂性对两者都有益。此外，我们强调训练数据中存在质量—多样性权衡，以及其对模型性能的下游影响。随后，我们考察合成数据流水线中各个组件对每项数据特征的影响。这一考察使我们能够根据合成数据生成算法所采用的组件及其对数据 QDC 构成产生的结果，对这些算法进行分类和比较。该分析进一步延伸到讨论在合成数据中平衡 QDC 对高效强化学习和自我改进算法的重要性。与训练数据中的 QD 权衡类似，模型输出质量与输出多样性之间通常也存在权衡，这些权衡会影响合成数据的构成。我们观察到，目前许多模型只针对输出质量进行评估和优化，从而限制了输出多样性和自我改进的潜力。我们认为，平衡这些权衡对于未来自我改进算法的发展至关重要，并重点介绍了若干在这一方向上取得进展的工作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
