Enhancing Data Privacy in Large Language Models through Private Association Editing

Large Language Models (LLMs) are powerful tools with extensive applications, but their tendency to memorize private information raises significant concerns as private data leakage can easily happen. In this paper, we introduce Private Association Editing (PAE), a novel defense approach for private data leakage. PAE is designed to effectively remove Personally Identifiable Information (PII) without retraining the model. Our approach consists of a four-step procedure: detecting memorized PII, applying PAE cards to mitigate memorization of private data, verifying resilience to targeted data extraction (TDE) attacks, and ensuring consistency in the post-edit LLMs. The versatility and efficiency of PAE, which allows for batch modifications, significantly enhance data privacy in LLMs. Experimental results demonstrate the effectiveness of PAE in mitigating private data leakage. We believe PAE will serve as a critical tool in the ongoing effort to protect data privacy in LLMs, encouraging the development of safer models for real-world applications.

A Trip Towards Fairness: Bias and De-Biasing in Large Language Models

An outbreak in the popularity of transformer-based Language Models (such as GPT (Brown et al., 2020) and PaLM (Chowdhery et al., 2022)) has opened the doors to new Machine Learning applications. In particular, in Natural Language Processing and how pre-training from large text, corpora is essential in achieving remarkable results in downstream tasks. However, these Language Models seem to have inherent biases toward certain demographics reflected in their training data. While research has attempted to mitigate this problem, existing methods either fail to remove bias altogether, degrade performance, or are expensive. This paper examines the bias produced by promising Language Models when varying parameters and pre-training data. Finally, we propose a de-biasing technique that produces robust de-bias models that maintain performance on downstream tasks.