Lightweight reasoning models for NER

Abstract

While large language model based approaches for Named Entity Recognition have advanced through fine-tuning and in-context learning, they often suffer from mediocre performance and a significant lack of interpretability. Although reasoning models employing Chain-of-Thought mechanisms demonstrate superior inferential capabilities, their practical deployment is hindered by high computational overhead and logical redundancy. To address these limitations, this paper proposes Lite- CoNER, a lightweight NER framework that achieves an effective balance between recognition accuracy and inference efficiency. By leveraging knowledge distillation and reasoning simplification, the framework transfers the deep reasoning expertise of teacher models—specifically in entity boundary determination and category logic—to lightweight student models. We first generate high-quality reasoning data through prompt engineering and introduce an LLM-based evaluator to prune logical redundancy and compress reasoning trajectories. Finally, we apply LoRA for parameter-efficient finetuning, enabling the student model to internalize the distilled reasoning capabilities. Experimental results across multiple benchmark datasets demonstrate that Lite-CoNER significantly outperforms traditional fine-tuning methods while maintaining high inference speeds. Furthermore, the simplified reasoning trajectories provide a transparent view of the decision-making process, proving that lightweight models can effectively inherit complex logic through structured distillation.