Summary of Benchmarking Advanced Text Anonymisation Methods: a Comparative Study on Novel and Traditional Approaches, by Dimitris Asimopoulos et al.
Benchmarking Advanced Text Anonymisation Methods: A Comparative Study on Novel and Traditional Approaches
by Dimitris Asimopoulos, Ilias Siniosoglou, Vasileios Argyriou, Thomai Karamitsou, Eleftherios Fountoukidis, Sotirios K. Goudos, Ioannis D. Moscholios, Konstantinos E. Psannis, Panagiotis Sarigiannidis
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A comprehensive benchmarking study is presented that compares the performance of transformer-based models and Large Language Models (LLMs) against traditional architectures for text anonymization tasks. The CoNLL-2003 dataset is utilized to evaluate several models, highlighting strengths and weaknesses of each approach. While modern models exhibit advanced capabilities in capturing contextual nuances, certain traditional architectures still maintain high performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Text anonymization using transformer-based models and Large Language Models (LLMs) is crucial for data privacy. This study compares the performance of these models against traditional architectures on the CoNLL-2003 dataset. The results show which models are best for anonymizing text, making it easier to choose a model for your needs. |
Keywords
» Artificial intelligence » Transformer
