$\textbf{Only-IF}$: Revealing the Decisive Effect of Instruction Diversity on Generalization
Keywords: Instruction Following; Generalization
Abstract: Understanding and accurately following instructions is critical for large language models (LLMs) to be effective across diverse tasks. In this work, we conduct a rigorous investigation into the factors that enable generalization to unseen instructions. 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. 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.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 9124
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