Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft
  Prompting and Calibrated Confidence Estimation
Zhexin Zhang Jiaxin Wen Minlie Huang
Large pre-trained language models achieve impressive results across many
tasks. However, recent works point out that pre-trained language models may
memorize a considerable fraction of their training data, leading to the privacy
risk of information leakage. In this paper, we propose a method named Ethicist
for targeted training data extraction through loss smoothed soft prompting and
calibrated confidence estimation, investigating how to recover the suffix in
the training data when given a prefix. To elicit memorization in the attacked
model, we tune soft prompt embeddings while keeping the model fixed. We further
propose a smoothing loss that smooths the loss distribution of the suffix
tokens to make it easier to sample the correct suffix. In order to select the
most probable suffix from a collection of sampled suffixes and estimate the
prediction confidence, we propose a calibrated confidence estimation method,
which normalizes the confidence of the generated suffixes with a local
estimation. We show that Ethicist significantly improves the extraction
performance on a recently proposed public benchmark. We also investigate
several factors influencing the data extraction performance, including decoding
strategy, model scale, prefix length, and suffix length. Our code is available
at https://github.com/thu-coai/Targeted-Data-Extraction.