LAUD: Integrating Large Language Models with Active Learning for Unlabeled Data

• URL: http://arxiv.org/abs/2511.14738v1
• Date: Tue, 18 Nov 2025 18:31:00 GMT
• Title: LAUD: Integrating Large Language Models with Active Learning for Unlabeled Data
• Authors: Tzu-Hsuan Chou, Chun-Nan Chou,
• Abstract summary: In real-world scenarios, lacking labeled data often prevents practitioners from obtaining well-performing models.<n>We present a learning framework integrating large language models with active learning for unlabeled dataset (LAUD)<n>Experiments show that LLMs derived from LAUD outperform LLMs with zero-shot or few-shot learning on commodity name classification tasks.
• Score: 2.0052993723676895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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