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---
license: mit
---
# 📰 NYT Datasets
The 📰**NYT datasets** serve as a benchmark designed to evaluate methods to identify memorized training data or to infer membership, specifically from OpenAI models that are released before 2023.
🔧 Memorized Training Data
This dataset contains examples provided in [Exhibit-J](https://nytco-assets.nytimes.com/2023/12/Lawsuit-Document-dkt-1-68-Ex-J.pdf) of the New York Times Lawsuit (label=1).
- Snippet is the article.
- Prefix is the prefix provided to the model according to the evidence in Exhibit-J.
- Completion is the original article content that follows the prefix.
🔧 Non-Member Data
This dataset also contains excerpts of CNN articles scraped in 2023 (label=0).
### 📌 Applicability
The datasets can be applied to OpenAI models released before **2023**.
## Loading the datasets
To load the dataset:
```python
from datasets import load_dataset
dataset = load_dataset("lasha-nlp/NYT_Memorization")
```
* *Label 0*: Refers to the CNN data that cannot have been memorized. *Label 1*: Refers to text data from NYT articles.
## 🛠️ Codebase
For evaluating information-guided probes, visit our [GitHub repository](https://github.com/AbhilashaRavichander/information-probing).
## ⭐ Citing our Work
If you find our code or data useful, kindly cite our work:
```bibtex
@misc{ravichander2025informationguidedidentificationtrainingdata,
title={Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models},
author={Abhilasha Ravichander and Jillian Fisher and Taylor Sorensen and Ximing Lu and Yuchen Lin and Maria Antoniak and Niloofar Mireshghallah and Chandra Bhagavatula and Yejin Choi},
year={2025},
eprint={2503.12072},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.12072},
}
```
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