$\textbf{Only-IF}$:Revealing the Decisive Effect of Instruction Diversity on Generalization (2410.04717v3)

Abstract: Understanding and accurately following instructions is critical for 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.

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

The paper titled "Only-IF: Revealing the Decisive Effect of Instruction Diversity on Generalization" focuses on a critical exploration of LLMs in relation to instruction-following capabilities. This research emphasizes the pivotal role of instruction diversity in enhancing the generalization abilities of LLMs to handle unseen tasks, which is particularly relevant for training models across diverse applications.

Overview and Methodology

The researchers examine instruction generalization through systematic experiments influenced by the Markov algorithm, a Turing-complete model. They adopt a symbolic task paradigm using string rewrites to isolate and test instruction-following mechanisms without conflating them with other abilities like reasoning. The paper rigorously tests the hypothesis that cross-domain semantic diversification can significantly enhance model adaptability to new instructions compared to mere intra-domain variation.

Two main settings are explored: training generalist LLMs for broad applications and specialist models focused on specific tasks, such as code generation. The researchers utilize controlled synthetic experiments to analyze semantic domains' diversity impact and extend analysis to real-world datasets like OSS-Instruct for code tasks.

Key Findings

1. Instruction Diversity as a Determinant of Generalization: The paper reveals a pronounced impact of diverse instructions on a model's capability to generalize. Crucially, models trained with cross-domain diversified instructions perform notably better compared to those trained on more extensive but less varied datasets.
2. Synthetic and Real-World Implications: The insights from synthetic rewriting tasks translate into real-world applications. For instance, specialist instruction-followers in code generation showed significant performance improvements upon introducing non-coding data, highlighting the utility of diverse semantic exposure.
3. Balancing Specialization and Generalization: The simulations exhibit that while high levels of domain-specific training (specialization) are beneficial, the inclusion of diversified data can further enhance a model's adaptability and performance, even with fewer domain-specific examples.
4. Real-World Model Training: Through training on datasets like UltraInteract-SFT, OpenOrca, and Alpaca, the research underscores the advantage of dataset diversification strategy over mere size expansion, notably in generalist settings.

Implications and Future Directions

Practical Implications: This paper provides valuable guidelines for dataset curation in instruction-tuning. It indicates that achieving optimal model performance in real-world applications necessitates embracing a strategy that includes diverse instructions across various domains. This approach is more effective than simply enlarging datasets with homogeneous data.

Theoretical Insights: The results emphasize the importance of semantic coverage in training LLMs, suggesting that models benefit from instruction diversity not only for unseen tasks but also in enhancing core capabilities such as instruction-following.

Speculation on Future Developments: As LLMs continue to integrate into complex environments, these insights could drive the evolution of more robust and adaptable AI systems. Future research may explore deeper into specific domains to map out optimal configurations of diverse instruction sets for different applications, further refining the balance between generalization and specialization.

This work signifies a meaningful advancement in understanding the dynamics of instruction tuning and model training, laying a foundation for future exploration in LLM development strategies.