Differentially Private Knowledge Distillation via Synthetic Text Generation (2024.findings-acl)
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| Challenge: | Large Language models (LLMs) are achieving state-of-the-art performance in many downstream tasks, but data privacy is a major challenge for practitioners. |
| Approach: | They propose a differentially private knowledge distillation algorithm that exploits the knowledge of a teacher LLM and a student's output distribution. |
| Outcome: | The proposed algorithm significantly improves the utility over baselines on the Big Patent dataset, with strong privacy parameters, =2. |
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