Bits for Privacy: Evaluating Post-Training Quantization via Membership Inference

This article, sourced from ArXiv, focuses on evaluating post-training quantization techniques through membership inference, likely assessing the privacy implications of these methods in the context of large language models (LLMs). The title suggests a focus on the trade-off between model compression (quantization) and privacy preservation. The use of membership inference indicates an attempt to determine if a specific data point was used in the model's training, a key privacy concern.

• •Focuses on the privacy implications of post-training quantization.
• •Uses membership inference to evaluate privacy.
• •Relevant to the field of LLMs.