Justine BEL-LETOILE

Anonymizing free-text data is harder than it seems. While structured databases have well-established anonymization techniques, textual data — like invoices, resumes, or medical records — poses unique challenges.

For some natural language processing (NLP) tasks, based on your production constraints, a simpler custom model can be a good contender to off-the-shelf large language models (LLMs), as long as you have enough qualitative data to build it. The stumbling block being how to obtain such data?