Running Large Language Models Privately - privateGPT and Beyond

Large Language Models (LLMs) have revolutionized how we access and consume information, shifting from a search engine market that was predominantly retrieval-based to one now that is growingly memory-based and performs generative search. However, the wide-scale adoption of LLMs raises concerns around privacy and data security. To leverage the advantages of generative AI while simultaneously addressing these privacy concerns, the field of privacy-preserving machine learning has emerged, offering techniques and tools such as federated learning, homomorphic encryption, and locally deployed LLMs. These approaches allow for the secure execution of large language models while protecting the confidentiality of sensitive data both during model fine-tuning as well as when providing responses grounded in proprietary data.

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