This repository hosts the components necessary for implementing all the methods proposed in the paper, Time Travel in LLMs: Tracing Data Contamination in Large Language Models, authored by Shahriar Golchin* and Mihai Surdeanu.

Our research is the first to systematically uncover and detect the issue of data contamination within the fully black-box large language models (LLMs). The primary idea revolves around the notion that if an LLM has seen a dataset instance during its pre-training phase, the LLM is able to replicate it. This is supported by two observations: (1) LLMs have enough capacity to memorize data; and (2) LLMs are trained to follow instructions effectively. However, due to the safety filters implemented in LLMs to prevent them from generating copyrighted content, explicitly asking LLMs to reproduce these instances is ineffective, as it triggers safety mechanisms. Our method circumvents these filters by replicating dataset instances given their random-length initial segments. Below is an example of our strategy in action, whereby the subsequent segment of an instance from the train split of the IMDB dataset is exactly replicated by GPT-4.

Start the process by cloning the repository, executing the command below:

git clone https://github.com/shahriargolchin/time-travel-in-llms.git

Afterward, proceed to the project's directory with this command:

cd time-travel-in-llms

Next, establish a conda environment to streamline the installation of required packages:

conda create --name time-travel-env python=3.11.8

Now, activate your environment:

conda activate time-travel-env

Lastly, use pip to install all the requisite packages:

pip install -r requirements.txt

git clone https://github.com/google-research/bleurt.git dependencies/bleurt_scorer
cd dependencies/bleurt_scorer
pip install .

Then, download the model checkpoint for BLEURT to use, by running the following command: (Note that we used the BLEURT-20 checkpoint for our study, and the provided command downloads this particular checkpoint. You can use any other checkpoint from the list available here.)

wget https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip
unzip BLEURT-20.zip

Alternatively, if you do not have wget installed, you can use the following command as an alternative:

curl -O https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip
unzip BLEURT-20.zip

For every scenario discussed in the paper, we provide a corresponding bash file in the scripts directory. Upon running these bash scripts, data contamination is detected for the examined subset of data. In the results directory, individual text files are generated for each evaluation method applied (such as ROUGE-L, BLEURT, and GPT-4 ICL) to display pass/fail results for contamination detection. The input CSV file, along with all intermediate results, is also stored in the same directory.

Before initializing the experiments, you need to export your OpenAI key to ensure the OpenAI models in this project (GPT-4 and GPT-3.5) can be accessed, with the following command:

export OPENAI_API_KEY=your-api-key

To run an experiment, first navigate to the scripts/dataset-name directory where bash scripts for each partition of a dataset (e.g., train, test/validation) are located. You can do this with the below command (assuming you are in the root directory):

cd scripts/dataset-name

Once inside the relevant directory, set the bash file to executable by running this command:

chmod +x bash-file-name.sh

Finally, run the experiment by executing:

./bash-file-name.sh

@article{DBLP:journals/corr/abs-2308-08493,
  author       = {Shahriar Golchin and
                  Mihai Surdeanu},
  title        = {Time Travel in LLMs: Tracing Data Contamination in Large Language
                  Models},
  journal      = {CoRR},
  volume       = {abs/2308.08493},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2308.08493},
  doi          = {10.48550/ARXIV.2308.08493},
  eprinttype    = {arXiv},
  eprint       = {2308.08493},
  timestamp    = {Thu, 24 Aug 2023 12:30:27 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2308-08493.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

If you are interested in the field of data contamination detection in LLMs, you might find our second paper, Data Contamination Quiz: A Tool to Detect and Estimate Contamination in Large Language Models (repo available here), particularly useful. In this paper, we present a novel method not only for detecting contamination in LLMs but also for estimating its amount in the fully closed-source LLMs. For reference, you can cite this paper using the standard citation format provided below:

@article{DBLP:journals/corr/abs-2311-06233,
  author       = {Shahriar Golchin and
                  Mihai Surdeanu},
  title        = {Data Contamination Quiz: {A} Tool to Detect and Estimate Contamination
                  in Large Language Models},
  journal      = {CoRR},
  volume       = {abs/2311.06233},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2311.06233},
  doi          = {10.48550/ARXIV.2311.06233},
  eprinttype    = {arXiv},
  eprint       = {2311.06233},
  timestamp    = {Wed, 15 Nov 2023 16:23:10 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2311-06233.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}