@inproceedings{
liu2024selectit,
title={Select{IT}: Selective Instruction Tuning for {LLM}s via Uncertainty-Aware Self-Reflection},
author={Liangxin Liu and Xuebo Liu and Derek F. Wong and Dongfang Li and Ziyi Wang and Baotian Hu and Min Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=QNieOPt4fg}
}
Instruction tuning (IT) is crucial to tailoring large language models (LLMs) towards human-centric interactions. Recent advancements have shown that the careful selection of a small, high-quality subset of IT data can significantly enhance the performance of LLMs.  Despite this, common approaches often rely on additional models or data, which increases costs and limits widespread adoption. In this work, we propose a novel approach, termed $\textit{SelectIT}$, that capitalizes on the foundational capabilities of the LLM itself. Specifically, we exploit the intrinsic uncertainty present in LLMs to more effectively select high-quality IT data, without the need for extra resources.  Furthermore, we introduce a curated IT dataset, the $\textit{Selective Alpaca}$, created by applying SelectIT to the Alpaca-GPT4 dataset. Empirical results demonstrate that IT using Selective Alpaca leads to substantial model ability enhancement. The robustness of SelectIT has also been corroborated in various foundation models and domain-specific tasks.  Our findings suggest that longer and more computationally intensive IT data may serve as superior sources of IT, offering valuable insights for future research in this area.  Data, code, and scripts are freely available at https://github.com/Blue-Raincoat/SelectIT.
SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection
Liangxin Liu Xuebo Liu Derek F. Wong Dongfang Li Ziyi Wang Baotian Hu Min Zhang