Towards Efficient Active Learning in NLP via Pretrained Representations
This addresses the problem of high computational costs in active learning for NLP practitioners, offering a more efficient method that is incremental in nature.
The paper tackles the computational inefficiency of active learning with large language models (LLMs) by using pretrained representations during the active learning loop and fine-tuning only after data acquisition, achieving similar performance to full fine-tuning while being orders of magnitude less expensive on text classification benchmarks with BERT and RoBERTa.
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.