With the advance of language models, privacy protection is receiving more
attention. Training data extraction is therefore of great importance, as it can
serve as a potential tool to assess privacy leakage. However, due to the
difficulty of this task, most of the existing methods are proof-of-concept and
still not effective enough. In this paper, we investigate and benchmark tricks
for improving training data extraction using a publicly available dataset.
Because most existing extraction methods use a pipeline of
generating-then-ranking, i.e., generating text candidates as potential training
data and then ranking them based on specific criteria, our research focuses on
the tricks for both text generation (e.g., sampling strategy) and text ranking
(e.g., token-level criteria). The experimental results show that several
previously overlooked tricks can be crucial to the success of training data
extraction. Based on the GPT-Neo 1.3B evaluation results, our proposed tricks
outperform the baseline by a large margin in most cases, providing a much
stronger baseline for future research. The code is available at
https://github.com/weichen-yu/LM-Extraction.Comment: ICML 202