Instruction Diversity Drives Generalization To Unseen Tasks

TL;DR

This paper demonstrates that increasing instruction diversity during tuning significantly improves a language model's ability to generalize to unseen tasks, even with limited training examples per task.

Contribution

It reveals that instruction diversity is a key factor for generalization, providing experimental evidence and insights into how diverse instruction sets enhance model robustness.

Findings

Instruction diversity enables generalization to unseen tasks.

Few examples per task suffice when instruction diversity is high.

Diverse instructions improve robustness against non-uniform training distributions.

Abstract

Instruction tuning -- fine-tuning a large language model (LLM) on pairs of instructions and desired outcomes -- is an approach that enables pre-trained language models to perform real-world tasks and follow human instructions. Its practical success depends on the model learning a broader set of instructions than those it was trained on. Yet the factors that determine model generalization to such \emph{unseen tasks} are not well understood. %To understand the driving factors of generalization, In this paper, we experiment with string rewrites, a symbolic task that serves as a building block for Turing complete Markov algorithms while allowing experimental control of "inputs" and "instructions". We investigate the trade-off between the number of instructions the model is trained on and the number of training samples provided for each instruction and observe that the diversity of the instruction set determines generalization. Generalization emerges once a diverse enough set of tasks is provided, even though very few examples are provided for each task. Instruction diversity also ensures robustness with respect to non-uniform distributions of instructions in the training set.