Towards Efficient Active Learning in NLP via Pretrained Representations
Keywords: Active Learning, Transfer Learning, NLP
TL;DR: We propose an active learning protocol that offers exceptional efficiency by leveraging representation spaces of pretrained Large Language Models.
Abstract: 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.
Primary Subject Area: Active learning, Data cleaning, acquisition for ML
Paper Type: Research paper: up to 8 pages
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Submission Number: 6
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