Anne Paling


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Data Anonymization for Privacy-Preserving Large Language Model Fine-Tuning on Call Transcripts
Shayna Gardiner | Tania Habib | Kevin Humphreys | Masha Azizi | Frederic Mailhot | Anne Paling | Preston Thomas | Nathan Zhang
Proceedings of the Workshop on Computational Approaches to Language Data Pseudonymization (CALD-pseudo 2024)

Large language models in public-facing industrial applications must accurately process data for the domain in which they are deployed, but they must not leak sensitive or confidential information when used. We present a process for anonymizing training data, a framework for quantitatively and qualitatively assessing the effectiveness of this process, and an assessment of the effectiveness of models fine-tuned on anonymized data in comparison with commercially available LLM APIs.