AccentMatch: Adaptive Competence Control via External Network Teaching

In this paper, we propose AccentMatch, a unified framework that distills the reasoning capabilities of Large Language Models (LLMs) into efficient Semi-Supervised Learning (SSL) students. Unlike standard distillation, AccentMatch introduces a dynamic teacher annealing mechanism based on training dynamics. We utilize an LLM as a teacher to generate high-quality initial pseudo-labels. We then fuse these with the student’s own predictions, dynamically adjusting the weight of the LLM guidance based on the student's learning status of unlabeled examples. This ensures the student relies on the LLM pseudo-label when it has a low learning status for an unlabeled example and transitions to self-training as it stabilizes. We show that our approach outperforms both state-of-the-art SSL baselines and few-shot LLMs across six text classification benchmarks, setting a new standard for efficient, high-accuracy classification in low-resource regimes.