SelectIT: Selective Instruction Tuning for Large Language Models via
  Uncertainty-Aware Self-Reflection
Liangxin Liu Xuebo Liu Derek F. Wong Dongfang Li Ziyi Wang Baotian Hu Min Zhang
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 sets, which increases costs and limits widespread
adoption. In this work, we propose a novel approach, termed 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 novel IT dataset, the 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.