How Much do Language Models Regurgitate under Post-Training Quantization?

Quantization has become a standard practice for efficient deployment of today's large language models. However, there is still a lack of understanding of its impact on verbatim regurgitation—where models may regurgitate training data samples, which can be a privacy vulnerability. In this work, we present the first comprehensive study of how post-training quantization affects verbatim regurgitation in LLMs, uncovering multiple key insights. First, we show that reducing precision consistently lowers regurgitation rate across all model sizes, but at the cost of decreased downstream performance. Second, we find that for the same set of sequences, larger quantized models regurgitate more than smaller models, consistent across varying prefix & suffix lengths. We also propose a scaling law to model verbatim regurgitation under quantization. Lastly, fine-grained layer-wise and component-wise analyses reveal a consistent trend: quantizing attention modules leads to a higher decrease in regurgitation compared to feed-forward networks, and quantizing later layers has a more pronounced impact than early-middle layers, further supported by flip rate analyses. Our empirical study covers three modern post-training quantization methods and six models from the Pythia suite, providing novel insights into the impact of quantization on verbatim regurgitation.