用大模型+主动学习，零人工标注实现高效跨任务文本分类
Applying LLMs to Active Learning: Towards Cost-Efficient Cross-Task Text Classification without Manually Labeled Data
- 用大模型生成伪标签，结合主动学习策略筛选关键样本
- 性能保留93%以上，计算成本降至6%左右
- 适合资源有限但需多任务分类的场景
基于机器学习的文本分类方法（如情感分析、新闻分类、有害评论识别）广泛使用，但其训练通常依赖大量人工标注数据，而人工标注耗时耗力且需领域知识，导致成本高昂。为解决此问题，本文提出一种将大语言模型（LLMs）融入主动学习框架的方法，在无需任何人工标注数据的情况下，实现高精度的跨任务文本分类。相比直接使用GPT进行分类，该方法在保持超过93%分类性能的同时，仅需约6%的计算时间和成本，显著提升了性能与资源效率的平衡。研究为大模型与主动学习算法在文本分类中的高效应用提供了新思路，推动其在更多场景下的落地。
原文摘要 · Abstract (English)
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, 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.
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