  Large "instruction-tuned" language models (i.e., finetuned to respond to
instructions) have demonstrated a remarkable ability to generalize zero-shot to
new tasks. Nevertheless, they depend heavily on human-written instruction data
that is often limited in quantity, diversity, and creativity, therefore
hindering the generality of the tuned model. We conducted a quantitative study
to figure out the efficacy of machine-generated annotations, where we compare
the results of a fine-tuned BERT model with human v/s machine-generated
annotations. Applying our methods to the vanilla GPT-3 model, we saw that
machine generated annotations were 78.54% correct and the fine-tuned model
achieved a 96.01% model performance compared to the performance with
human-labelled annotations. This result shows that machine-generated
annotations are a resource and cost effective way to fine-tune down-stream
models.
