datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
gretel-safety-alignment-en-v1
Gretel Synthetic Safety Alignment Dataset
This dataset is a synthetically generated collection of prompt-response-safe_response triplets that can be used for aligning language models. Created using Gretel Navigator's AI Data Designer using small language models like ibm-granite/granite-3.0-8b, ibm-granite/granite-3.0-8b-instruct, Qwen/Qwen2.5-7B, Qwen/Qwen2.5-7B-instruct and mistralai/Mistral-Nemo-Instruct-2407.
Dataset Statistics
Total Records: 8,361
Total… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/gretel-safety-alignment-en-v1.gretel-safety-alignment-es-v1gretelai/gretel-safety-alignment-en-v1 but with the prompt, response, and safe_response fields translated to spanish. Translation was done using gpt4o-mini for most rows, and mistral-small-3.2-24b-instruct for those gpt4o-mini refused to translate.
Some rows in gretelai/gretel-safety-alignment-en-v1 contained model refusals in the prompt or response fields. Those were filtered out, and thus are not included in this dataset.
Role-of-Provider-on-Safety-Alignment-in-Large-Language-Models
Evaluating the Role of Provider on Safety Alignment in Large Language Models: dataset
Data for the paper
Naser, M.Z. (2026). Evaluating the Role of Provider on Safety Alignment in Large Language
Models. Neurocomputing, 135173. https://doi.org/10.1016/j.neucom.2026.135173
It holds the Extended Context Safety Benchmark (ECSB) scenario bank and every trial result.
If you use the data, please cite the paper (BibTeX under Citation).
The metadata.paper field inside… See the full description on the dataset page: https://huggingface.co/datasets/mznaser/Role-of-Provider-on-Safety-Alignment-in-Large-Language-Models.lrm_safety_alignment_dpoclinical-quad-guidance-alignment-claim-strength-safety-signal-certainty-regulatory-risk-v0.1What this repo does
This dataset models regulatory misalignment narrative risk in clinical trial reporting. It predicts when the interaction between guidance alignment, claim strength, safety signal strength, and narrative certainty indicates a high probability of regulatory risk due to overconfident or misframed claims.
Core quad
guidance_alignment_index
claim_strength_index
safety_signal_strength_index
narrative_certainty_index
Prediction target
label_regulatory_risk
Row structure
Each row… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-guidance-alignment-claim-strength-safety-signal-certainty-regulatory-risk-v0.1.gretel-safety-alignment-en-v1