Extracting Training Data from Large Langauge Models

Extracting Training Data from Large Langauge Models

A re-implementation of the "Extracting Training Data from Large Language Models" paper by Carlini et al. The paper already has an official implementation - https://github.com/ftramer/LM_Memorization, from which I have borrowed parts of the code, at the same time improving the readability of a few functions.

However, the official repository does not cover -

• Sampling Method - Sampling With A Decaying Temperature (Section 5.1.1 of the paper)
• Metric - Minimum perplexity on a Sliding Window (Section 5.2 of the paper)

I was really fascinated with the paper and wanted to implement it myself. Like the official implementation, I have also included a Samples.md file, which has some of the memorized content that I could extract from GPT-2. Although I am able to find some interesting memorized content, the results still have a few limitations -

• Due to compute time constraints, I could only generate 25,000 samples for each sampling method (as compared to 600,000 generated by the paper authors)
• Due to memory constraints, I was not able to incorporate the metric - ratio of log-perplexities of GPT2-XL and GPT2-Medium. I have included the code for that in script and if one has sufficient compute, they can uncomment the relevant lines and incorporate that metric as well.

Requirements

• PyTorch
• Transformers
• Numpy
• Tqdm

Or, directly

pip install -r requirements.txt

Extracting Data

Metrics for Ranking

The generated samples are ranked according to six membership inference metrics introduced in the paper:

• The log-perplexity of the GPT2-XL model
• The ratio of the log-perplexities of the GPT2-XL model and the GPT2-Small model
• The ratio of the log-perplexities of the GPT2-XL model and the GPT2-Medium model (implemented but not couldn't be run due to compute constraints)
• The ratio of the log perplexity of GPT2-XL and the sample's entropy estimated by Zlib
• The ratio of the log-perplexities of the GPT2-XL for the generated sample and the same sample in lower-case letters
• The minimum log-perplexity of GPT2-XL on window of size 50

The top 10 samples according to each metric are printed out, and the top 100 samples according to each metric ae logged in the outfile. These samples are likely to contain verbatim text from the GPT-2 training data.

Top-k sampling

python extraction_top_n.py --N 5000 --batch_size 20 --outfile top_n_samples.txt

This generates 5000 samples with GPT2-XL. The samples are generated with top-k sampling (k=40) and an empty prompt.

Temperature Decay

python extraction_temperature_decay.py --N 5000 --batch_size 20 --outfile temperature_decay_samples.txt

This generates 5000 samples with GPT2-XL. The samples are generated with sampling with temperature decay (decay the softmax temperature from 10 to 1 or the first 20 tokens and 1 for all subsequent tokens) and an empty prompt.

Conditioning on Internet text

In the paper, the authors also tried prompting the GT2-XL model with snippets of text from the web (commoncrawl) which increased the chance of the model generating memorized content.

I used the same sample of the Crawl from May 2021 (~350 MB) used by the authors.

./download_cc.sh

Then,

python extraction_commoncrawl.py --N 5000 --batch_size 20 --outfile commoncrawl_samples.txt

All the generated sequences have a final length of atmost 256 tokens.

Sample outputs

Some interesting outputs that were extracted from GPT-2 can be found here.