Privacy-Preserving and Trustworthy Fine-Tuning of Large Language Models

Fine-tuning of large language models has been a common approach for adjusting these models for specific domains of their intended applications, but still, there are numerous issues related to the safe use of these models. Fine-tuning these models with sensitive or private data can result in the leakage of private data from these models in attacks such as membership inference attacks, gradient leakage attacks, or model inversion attacks. In addition to privacy-related concerns, fine-tuned language models are also faced with trust-related concerns due to their potential for being or becoming racist, toxic, or unverifiable. Furthermore, the efficiency of fine-tuning these models for real-world applications is also faced with efficiency constraints due to the computational requirements of these models. These issues have been addressed in the current state of the art in different ways, but the solutions for the above-mentioned problems are still traditionally addressed individually in their respective domains. With the help of the above-mentioned gaps in the related works, the proposed work aims to introduce a complete framework for differential privacy, data augmentation for synthetic data, model compression, as well as trust evaluation for the fine-tuning of language models.