Datasets:

killdevil111
/

DataShield-Sample-Risk

pretty_name: DataShield Sample-Level Risk Scores
language:
  - en
license: other
task_categories:
  - text-generation
tags:
  - safety
  - alignment
  - instruction-tuning
  - data-filtering
  - risk-scoring
  - emnlp
size_categories:
  - 10K<n<100K
configs:
  - config_name: dolly15k
    default: true
    data_files:
      - split: train
        path: data/dolly15k.jsonl
  - config_name: alpaca52k
    data_files:
      - split: train
        path: data/alpaca52k.jsonl

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.

DataShield framework

Dataset configurations

Configuration	Source dataset	Rows
`dolly15k`	`databricks/databricks-dolly-15k`	15,011
`alpaca52k`	`tatsu-lab/alpaca`	51,974

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}
}