$\textbf{Only-IF}$:Revealing the Decisive Effect of Instruction Diversity on Generalization
Dylan Zhang, Justin Wang, Francois Charton
2024-10-09
Summary
This paper discusses how the diversity of instructions given to large language models (LLMs) affects their ability to understand and follow new instructions. It emphasizes the importance of having a wide range of different types of instructions during training.
What's the problem?
Many LLMs struggle to generalize their understanding to new instructions, which means they can't perform well on tasks they haven't seen before. This is often because the training data they receive lacks sufficient variety in the types of instructions, limiting their ability to adapt to different scenarios.
What's the solution?
The authors conducted experiments to show that LLMs only improve their ability to handle new instructions when they are trained on a diverse set of examples from various topics. They found that just mixing up similar types of instructions isn't enough; instead, including different kinds of tasks and instructions from various domains leads to better performance. They also discovered that increasing the diversity of an existing dataset can enhance model performance without needing more data overall.
Why it matters?
This research is important because it provides clear guidelines for improving how LLMs are trained, which can lead to better performance in real-world applications. By focusing on diversifying instruction data, developers can create more adaptable and effective AI systems that can handle a wider range of tasks and instructions.
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.