In the realm of data privacy, the ability to effectively anonymise text is
paramount. With the proliferation of deep learning and, in particular,
transformer architectures, there is a burgeoning interest in leveraging these
advanced models for text anonymisation tasks. This paper presents a
comprehensive benchmarking study comparing the performance of transformer-based
models and Large Language Models(LLM) against traditional architectures for
text anonymisation. Utilising the CoNLL-2003 dataset, known for its robustness
and diversity, we evaluate several models. Our results showcase the strengths
and weaknesses of each approach, offering a clear perspective on the efficacy
of modern versus traditional methods. Notably, while modern models exhibit
advanced capabilities in capturing con textual nuances, certain traditional
architectures still keep high performance. This work aims to guide researchers
in selecting the most suitable model for their anonymisation needs, while also
shedding light on potential paths for future advancements in the field.

本研究通过对比分析使用转换器模型和大型语言模型 (LLM) 与传统架构进行文本匿名化任务的性能，评估了几种模型的表现，并展示了每种方法的优点和缺点，为研究人员选择最合适的模型提供了明确的视角。