  The remarkable performance of large language models (LLMs) in zero-shot
language understanding has garnered significant attention. However, employing
LLMs for large-scale inference or domain-specific fine-tuning requires immense
computational resources due to their substantial model size. To overcome these
limitations, we introduce a novel method, namely GenCo, which leverages the
strong generative power of LLMs to assist in training a smaller and more
adaptable language model. In our method, an LLM plays an important role in the
self-training loop of a smaller model in two important ways. Firstly, the LLM
is used to augment each input instance with a variety of possible
continuations, enriching its semantic context for better understanding.
Secondly, it helps crafting additional high-quality training pairs, by
rewriting input texts conditioned on predicted labels. This ensures the
generated texts are highly relevant to the predicted labels, alleviating the
prediction error during pseudo-labeling, while reducing the dependency on large
volumes of unlabeled text. In our experiments, GenCo outperforms previous
state-of-the-art methods when only limited ($<5\%$ of original) in-domain text
data is available. Notably, our approach surpasses the performance of Alpaca-7B
with human prompts, highlighting the potential of leveraging LLM for
self-training.
