Improving Zero-Shot Generalization of Instruction Tuning by Data Arrangement

Bingxiang He, Ning Ding, Cheng Qian, Jia Deng, Ganqu Cui, Lifan Yuan, Haiwen Hong, Huan-ang Gao, Longtao Huang, Hui Xue', Huimin Chen, Zhiyuan Liu, Maosong Sun

Keywords: zero-shot, instruction tuning, large language models

TL;DR: We investigate the phenomenon of zero-shot generalization during instruction tuning and highlight that better data arrangement plays a crucial role in improving it.

Abstract: Understanding alignment techniques begins with comprehending zero-shot generalization brought by instruction tuning, but little of the mechanism has been understood. Existing work has largely been confined to the task level, without considering that tasks are artificially defined and, to LLMs, merely consist of tokens and representations. To bridge this gap, we investigate zero-shot generalization from the perspective of the data itself. We first demonstrate that zero-shot generalization happens very early during instruction tuning, with loss serving as a stable indicator. Next, we investigate the facilitation of zero-shot generalization by data arrangement through similarity and granularity perspectives, confirming that encountering highly similar and fine-grained training data earlier during instruction tuning, without the constraints of defined ``tasks'', enables better generalization. Finally, we propose a more grounded training data arrangement method, Test-centric Multi-turn Arrangement, and show its effectiveness in promoting continual learning and further loss reduction. For the first time, we show that zero-shot generalization during instruction tuning is a form of similarity-based generalization between training and test data at the instance level. We hope our analysis will advance the understanding of zero-shot generalization during instruction tuning and contribute to the development of more aligned LLMs.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 13249