Accurate information extraction from specialized texts is a critical challenge for automated rule checking (ARC) in the architecture, engineering, and construction (AEC) domain. While large language models (LLMs) possess strong reasoning capabilities, their deployment in resource-constrained AEC environments is often impractical. Conversely, standard efficient models struggle with the significant domain gap. Although this gap can be mitigated by pre-training on large, humancurated corpora, such approaches are labor-intensive and costly. To address this, we propose ARCE (Augmented RoBERTa with Contextualized Elucidations), a novel knowledge distillation framework that leverages LLMs to synthesize a task-oriented corpus, termed Cote, for incrementally pre-training smaller models. ARCE systematically explores the optimal strategy for knowledge transfer. Our extensive experiments demonstrate that ARCE establishes a new state-of-the-art on a benchmark AEC dataset, achieving a Macro-F1 score of 77.20% and outperforming both domain-specific baselines and fine-tuned LLMs. Crucially, our study reveals a less is more principle: simple, direct explanations prove significantly more effective for domain adaptation than complex, role-based rationales in the NER task, which tend to introduce semantic noise. The source code will be made publicly available upon acceptance.

翻译：在建筑、工程与施工（AEC）领域的自动化规则检查（ARC）中，从专业文本中准确提取信息是一项关键挑战。尽管大语言模型（LLMs）具备强大的推理能力，但在资源受限的AEC环境中部署往往不切实际。相反，标准的高效模型则难以应对显著的领域差异。虽然通过在海量人工标注语料上进行预训练可以缓解这一差异，但此类方法人力成本高昂且耗费资源。为此，我们提出ARCE（基于上下文增强的RoBERTa模型），这是一种新颖的知识蒸馏框架，通过利用LLMs合成面向任务的语料库（称为Cote），对小型模型进行增量式预训练。ARCE系统性地探索了知识迁移的最优策略。大量实验表明，ARCE在基准AEC数据集上实现了新的最优性能，宏观F1分数达到77.20%，超越了领域专用基线模型及经微调的LLMs。更重要的是，本研究揭示了“少即是多”原则：在NER任务中，简单直接的解释对于领域适应的效果显著优于基于角色的复杂推理，后者往往会引入语义噪声。源代码将在论文录用后公开发布。