arxiv:2410.04717

Only-IF:Revealing the Decisive Effect of Instruction Diversity on Generalization

Published on Oct 7

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shizhuo2 on Oct 9

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Authors:

Dylan Zhang ,

Abstract

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 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 $textbf{specialist} and textbf{generalist}$ 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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shizhuo2

Paper author Paper submitter 30 days ago

Understanding and accurately following instructions is critical for large language models (LLMs) to perform effectively across a wide range of tasks. This work rigorously examines the factors enabling models to generalize to unseen instructions, providing valuable insights into optimizing data collection for instruction-tuning. By conducting controlled experiments inspired by the Turing-complete Markov algorithm, it becomes evident that generalization only emerges when the training data encompasses sufficient diversity across semantic domains. In contrast, limiting data diversification to narrow domains proves insufficient for achieving robust generalization. Cross-domain data diversification, even with constrained data budgets, markedly improves a model’s adaptability.

The analysis extends to real-world applications involving fine-tuning of both specialist and generalist models. In both cases, superior performance is achieved by increasing dataset diversity while maintaining constant data size. Moreover, when scaling up data, prioritizing semantic variety in instructions proves more effective than simply increasing the volume of similar data. These findings emphasize the importance of dataset curation strategies that enhance model performance across diverse scenarios. For specialist models, broadening the data beyond the core domain leads to substantial improvements, while generalist models thrive on a mixture of diverse data that strengthens their instruction-following capabilities across a wide array of tasks.

This research underscores the importance of strategic diversification when constructing datasets and offers practical guidelines for improving data quality, which in turn enhances the overall generalization capabilities of LLMs.