Model-Based Differentially Private Knowledge Transfer for Large Language Models

As large language models (LLMs) become increasingly prevalent The widespread adoption of large language models (LLMs) in web in web services, effectively leveraging domain-specific knowledge services has profoundly impacted various domains, yet their application while ensuring privacy has become critical. Existing methods, such in specialized domains - especially those handling sensitive as retrieval-augmented generation (RAG) and differentially private data - faces significant hurdles. State-of-the-art LLMs, such as GPT-data synthesis, often compromise either the utility of domain knowledge 4 [29] and Gemini [41], are typically closed-source and owned by or the privacy of sensitive data, limiting their applicability large companies (referred to as servers). These models, trained on in specialized domains. To address these challenges, we propose extensive public datasets, frequently struggle to deliver accurate Llamdex, a novel framework that integrates privacy-preserving, results in specialized areas like healthcare and finance, where precision domain-specific models into LLMs. Our approach significantly enhances is crucial. For example, a misdiagnosis in medical contexts the accuracy of domain-specific tasks, achieving up to a can pose serious health risks, while erroneous financial forecasts 26% improvement compared to existing methods under the same can lead to substantial economic implications.