Abstract
Machine learning-based classifiers have been used for text classification, such as sentiment analysis, news classification, and toxic comment classification. However, supervised machine learning models often require large amounts of labeled data for training, and manual annotation is both labor-intensive and requires domain-specific knowledge, leading to relatively high annotation costs. To address this issue, we propose an approach that integrates large language models (LLMs) into an active learning framework. Our approach combines the Robustly Optimized BERT Pretraining Approach (RoBERTa), Generative Pre-trained Transformer (GPT), and active learning, achieving high cross-task text classification performance without the need for any manually labeled data. Furthermore, compared to directly applying GPT for classification tasks, our approach retains over 93% of its classification performance while requiring only approximately 6% of the computational time and monetary cost, effectively balancing performance and resource efficiency. These findings provide new insights into the efficient utilization of LLMs and active learning algorithms in text classification tasks, paving the way for their broader application.
Abstract (translated)
基于机器学习的分类器已被用于文本分类，如情感分析、新闻分类和有毒评论分类。然而，监督式机器学习模型通常需要大量标注数据进行训练，并且手动标注既耗时又需要特定领域的知识，导致标注成本相对较高。为了解决这个问题，我们提出了一种将大型语言模型（LLM）集成到主动学习框架中的方法。我们的方法结合了稳健优化的BERT预训练方法（RoBERTa）、生成式预训练变换器（GPT）和主动学习，在无需任何人工标注数据的情况下实现了跨任务的高性能文本分类效果。此外，与直接应用GPT进行分类任务相比，我们的方法在保持超过93%分类性能的同时，仅需大约6%的计算时间和成本，有效平衡了性能和资源效率。这些发现为大型语言模型和主动学习算法在文本分类任务中的高效利用提供了新的见解，并为其更广泛的应用铺平道路。
URL
https://arxiv.org/abs/2502.16892
