Smaller Language Models Are Better Instruction Evolvers

TL;DR

This paper demonstrates that smaller language models can be more effective than larger ones in instruction evolution, producing more diverse and complex instructions, challenging the assumption that bigger models are inherently better for this task.

Contribution

The study reveals that smaller language models outperform larger ones in instruction synthesis and introduces a new metric, IC-IFD, to better evaluate instruction complexity and effectiveness.

Findings

Smaller models generate more effective instructions.

SLMs have a broader output space during instruction evolution.

Existing metrics do not adequately measure instruction impact.

Abstract

Instruction tuning has been widely used to unleash the complete potential of large language models. Notably, complex and diverse instructions are of significant importance as they can effectively align models with various downstream tasks. However, current approaches to constructing large-scale instructions predominantly favour powerful models such as GPT-4 or those with over 70 billion parameters, under the empirical presumption that such larger language models (LLMs) inherently possess enhanced capabilities. In this study, we question this prevalent assumption and conduct an in-depth exploration into the potential of smaller language models (SLMs) in the context of instruction evolution. Extensive experiments across three scenarios of instruction evolution reveal that smaller language models (SLMs) can synthesize more effective instructions than LLMs. Further analysis demonstrates that SLMs possess a broader output space during instruction evolution, resulting in more complex and diverse variants. We also observe that the existing metrics fail to focus on the impact of the instructions. Thus, we propose Instruction Complex-Aware IFD (IC-IFD), which introduces instruction complexity in the original IFD score to evaluate the effectiveness of instruction data more accurately. Our source code is available at: \href{https://github.com/HypherX/Evolution-Analysis}{https://github.com/HypherX/Evolution-Analysis}