/plushcap/analysis/gretel-ai/gretel-ai-fine-tuning-models-for-healthcare-via-differentially-private-synthetic-text

Fine-tuning Models for Healthcare via Differentially-Private Synthetic Text

What's this blog post about?

This blogpost discusses a method for fine-tuning large language models (LLMs) on specialized domains like healthcare while ensuring data privacy. The approach involves generating differentially-private synthetic text using Gretel's GPT model, which is then used to fine-tune LLMs for generating responses. Differential privacy provides formal guarantees that no training data will be ever extracted from the model, thus protecting sensitive information. The method was demonstrated by fine-tuning a Claude 3 Haiku model for generating clinical notes with the input of a transcript of conversation between a doctor and a patient.

Company
Gretel.ai

Date published
Oct. 29, 2024

Author(s)
Andre Manoel, Lipika Ramaswamy, Maarten Van Segbroeck, Qiong Zhang (AWS), Shashi Raina (AWS)

Word count
2238

Hacker News points
None found.

Language
English


By Matt Makai. 2021-2024.