LLM-Guided Co-Training for Text Classification

• URL: http://arxiv.org/abs/2509.16516v2
• Date: Tue, 23 Sep 2025 02:26:35 GMT
• Title: LLM-Guided Co-Training for Text Classification
• Authors: Md Mezbaur Rahman, Cornelia Caragea,
• Abstract summary: We introduce a novel weighted co-training approach guided by Large Language Models (LLMs)<n>We use LLM labels on unlabeled data as target labels and co-train two encoder-only based networks that train each other over multiple iterations.<n>By strategically utilizing LLM-generated guidance, our approach significantly outperforms conventional SSL methods.
• Score: 51.59706902936793
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce a novel weighted co-training approach that is guided by Large Language Models (LLMs). Namely, in our co-training approach, we use LLM labels on unlabeled data as target labels and co-train two encoder-only based networks that train each other over multiple iterations: first, all samples are forwarded through each network and historical estimates of each network's confidence in the LLM label are recorded; second, a dynamic importance weight is derived for each sample according to each network's belief in the quality of the LLM label for that sample; finally, the two networks exchange importance weights with each other -- each network back-propagates all samples weighted with the importance weights coming from its peer network and updates its own parameters. By strategically utilizing LLM-generated guidance, our approach significantly outperforms conventional SSL methods, particularly in settings with abundant unlabeled data. Empirical results show that it achieves state-of-the-art performance on 4 out of 5 benchmark datasets and ranks first among 14 compared methods according to the Friedman test. Our results highlight a new direction in semi-supervised learning -- where LLMs serve as knowledge amplifiers, enabling backbone co-training models to achieve state-of-the-art performance efficiently.

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