killdevil111/DataShield-Sample-Risk
DataShield This dataset releases sample-level risk scores for DataShield: Uncovering Risky Fine-Tuning Data Across LLMs Through Consensus Subspace Alignment, accepted to the EMNLP Main Conference. For the method, code, and complete documentation, see the DataShield GitHub repository. Dataset configurations Configuration Source dataset Rows dolly15k databricks/databricks-dolly-15k 15,011 alpaca52k tatsu-lab/alpaca 51,974 from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/killdevil111/DataShield-Sample-Risk.
DataShield
This dataset releases sample-level risk scores for DataShield: Uncovering Risky Fine-Tuning Data Across LLMs Through Consensus Subspace Alignment, accepted to the EMNLP Main Conference.
For the method, code, and complete documentation, see the DataShield GitHub repository.
<p align="center"> <img src="assets/framework.png" width="900" alt="DataShield framework"> </p>
Dataset configurations
from datasets import load_dataset
dolly = load_dataset("killdevil111/DataShield-Sample-Risk", "dolly15k")
alpaca = load_dataset("killdevil111/DataShield-Sample-Risk", "alpaca52k")Each record contains the source instruction-tuning sample and its final DataShield risk_score. A higher score indicates a higher estimated fine-tuning risk. The score is intended for ranking and data filtering rather than as a calibrated probability or binary label.
Citation
@article{wu2026datashield,
title={DataShield: Uncovering Risky Fine-Tuning Data Across LLMs Through Consensus Subspace Alignment},
author={Wu, Zefeng and Qi, Weiwei and Chen, Jielong and Zheng, Tianhang and Hong, Di and Lu, Chaochao and He, Liang and Qin, Zhan and Ren, Kui},
journal={arXiv preprint arXiv:2607.15081},
year={2026}
}