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.

翻译：在数据隐私领域，有效实现文本匿名化的能力至关重要。随着深度学习尤其是Transformer架构的普及，利用这些先进模型处理文本匿名化任务正引发越来越多的关注。本文通过全面的基准测试研究，将基于Transformer的模型及大型语言模型（LLM）与传统架构在文本匿名化性能上进行了比较。我们采用以稳健性和多样性著称的CoNLL-2003数据集，对多种模型进行了评估。研究结果揭示了每种方法的优劣势，清晰呈现了现代方法与传统方法的效能差异。值得注意的是，现代模型在捕捉上下文细微差别方面展现出先进能力，但某些传统架构仍能保持较高性能。本研究旨在指导研究者根据匿名化需求选择最合适的模型，同时为未来该领域的潜在发展路径提供启示。