  Enhancing the instruction-following ability of Large Language Models (LLMs)
primarily demands substantial instruction-tuning datasets. However, the sheer
volume of these imposes a considerable computational burden and annotation
cost. To investigate a label-efficient instruction tuning method that allows
the model itself to actively sample subsets that are equally or even more
effective, we introduce a self-evolving mechanism DiverseEvol. In this process,
a model iteratively augments its training subset to refine its own performance,
without requiring any intervention from humans or more advanced LLMs. The key
to our data sampling technique lies in the enhancement of diversity in the
chosen subsets, as the model selects new data points most distinct from any
existing ones according to its current embedding space. Extensive experiments
across three datasets and benchmarks demonstrate the effectiveness of
DiverseEvol. Our models, trained on less than 8% of the original dataset,
maintain or improve performance compared with finetuning on full data. We also
provide empirical evidence to analyze the importance of diversity in
instruction data and the iterative scheme as opposed to one-time sampling. Our
code is publicly available at https://github.com/OFA-Sys/DiverseEvol.git.
