  Dataset distillation aims to compress a training dataset by creating a small
number of informative synthetic samples such that neural networks trained on
them perform as well as those trained on the original training dataset. Current
text dataset distillation methods create each synthetic sample as a sequence of
word embeddings instead of a text to apply gradient-based optimization;
however, such embedding-level distilled datasets cannot be used for training
other models whose word embedding weights are different from the model used for
distillation. To address this issue, we propose a novel text dataset
distillation approach, called Distilling dataset into Language Model (DiLM),
which trains a language model to generate informative synthetic training
samples as text data, instead of directly optimizing synthetic samples. We
evaluated DiLM on various text classification datasets and showed that
distilled synthetic datasets from DiLM outperform those from current coreset
selection methods. DiLM achieved remarkable generalization performance in
training different types of models and in-context learning of large language
models. Our code will be available at https://github.com/arumaekawa/DiLM.
