  Fine-tuning Large Language Models (LLMs) is now a common approach for text
classification in a wide range of applications. When labeled documents are
scarce, active learning helps save annotation efforts but requires retraining
of massive models on each acquisition iteration. We drastically expedite this
process by using pretrained representations of LLMs within the active learning
loop and, once the desired amount of labeled data is acquired, fine-tuning that
or even a different pretrained LLM on this labeled data to achieve the best
performance. As verified on common text classification benchmarks with
pretrained BERT and RoBERTa as the backbone, our strategy yields similar
performance to fine-tuning all the way through the active learning loop but is
orders of magnitude less computationally expensive. The data acquired with our
procedure generalizes across pretrained networks, allowing flexibility in
choosing the final model or updating it as newer versions get released.
