用大模型+主动学习，解决无标签数据训练难题
LAUD: Integrating Large Language Models with Active Learning for Unlabeled Data
- 结合大模型与主动学习，从零开始构建初始标注集
- 在商品名称分类任务中优于零样本和少样本方法
- 适合缺乏标注数据的实践场景，降低人工调参成本
大语言模型（LLMs）展现出超越预训练数据的泛化能力，微调后可达到甚至超过人类水平的表现。但在实际应用中，缺乏标注数据常导致无法获得高性能模型，迫使从业者依赖繁琐、低效且试错驱动的提示工程方法。为缓解这一问题，本文提出一种将大语言模型与主动学习结合的框架——LAUD，用于处理无标签数据集。LAUD通过零样本学习构建初始标注集，缓解冷启动问题。实验结果表明，在商品名称分类任务中，基于LAUD训练的大模型性能优于零样本和少样本方法，验证了该框架的有效性。
原文摘要 · Abstract (English)
Large language models (LLMs) have shown a remarkable ability to generalize beyond their pre-training data, and fine-tuning LLMs can elevate performance to human-level and beyond. However, in real-world scenarios, lacking labeled data often prevents practitioners from obtaining well-performing models, thereby forcing practitioners to highly rely on prompt-based approaches that are often tedious, inefficient, and driven by trial and error. To alleviate this issue of lacking labeled data, we present a learning framework integrating LLMs with active learning for unlabeled dataset (LAUD). LAUD mitigates the cold-start problem by constructing an initial label set with zero-shot learning. Experimental results show that LLMs derived from LAUD outperform LLMs with zero-shot or few-shot learning on commodity name classification tasks, demonstrating the effectiveness of LAUD.
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