Only-IF：揭示指令多样性对泛化的决定性影响
$\textbf{Only-IF}$:Revealing the Decisive Effect of Instruction Diversity on Generalization
AI总结：
本文通过受图灵完备马尔可夫算法启发的对照实验，证明跨语义领域的数据多样化是模型泛化到未见指令的关键因素，并指出在专家和通才模型微调中，提高指令多样性比单纯增加相似数据数量更能有效提升性能。
摘要：
理解和准确遵循指令对于大语言模型（LLMs）在各种任务中有效发挥作用至关重要。在这项工作中，我们严格考察了使模型能够泛化到未见指令的关键因素，为指导指令微调数据的收集提供了见解。通过受图灵完备马尔可夫算法启发的对照实验，我们证明这种泛化只有在训练数据在语义领域上足够多样化时才会出现。我们的发现还揭示，仅仅在有限领域内进行多样化并不能确保稳健的泛化。相比之下，跨领域的数据多样化，即使在受限的数据预算下，也能显著增强模型的适应性。我们进一步将分析扩展到现实场景，包括对专家模型和通才模型的微调。在这两种情况下，我们都证明：1）在保持数据规模不变的情况下，通过提高现有数据集的多样性可以获得更好的性能；2）在扩大数据规模时，使指令的语义多样化比单纯增加相似数据的数量更有效。我们的研究为数据集整理提供了重要见解，特别是在通过扩展训练数据来优化专家和通才场景下模型性能时。我们表明，仔细考虑数据多样化是关键：用超出其核心领域的数据训练专家模型会带来显著的性能提升，而通才模型则受益于多样化的数据混合，从而增强其在广泛应用中的整体指令遵循能力。我们的结果突出了战略性多样化的关键作用，并为提高数据质量提供了明确的指导方针。
英文摘要：
Understanding and accurately following instructions is critical for large language models (LLMs) to be effective across diverse tasks. In this work, we rigorously examine the key factors that enable models to generalize to unseen instructions, providing insights to guide the collection of data for instruction-tuning. Through controlled experiments, inspired by the Turing-complete Markov algorithm, we demonstrate that such generalization $\textbf{only emerges}$ when training data is diversified enough across semantic domains. Our findings also reveal that merely diversifying within limited domains fails to ensure robust generalization. In contrast, cross-domain data diversification, even under constrained data budgets, significantly enhances a model's adaptability. We further extend our analysis to real-world scenarios, including fine-tuning of $\textit{$\textbf{specialist}$}$ and $\textit{$\textbf{generalist}$}$ models. In both cases, we demonstrate that 1) better performance can be achieved by increasing the diversity of an established dataset while keeping the data size constant, and 2) when scaling up the data, diversifying the semantics of instructions is more effective than simply increasing the quantity of similar data. Our research provides important insights for dataset collation, particularly when optimizing model performance by expanding training data for both specialist and generalist scenarios. We show that careful consideration of data diversification is key: training specialist models with data extending beyond their core domain leads to significant performance improvements, while generalist models benefit from diverse data mixtures that enhance their overall instruction-following capabilities across a wide range of applications. Our results highlight the critical role of strategic diversification and offer clear guidelines for improving data quality.
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