| --- |
| license: other |
| license_name: restricted-research-license |
| license_link: LICENSE |
| language: |
| - uk |
| - en |
| - ru |
| pretty_name: Ukrainian LLM Safety Dataset |
| size_categories: |
| - 10K<n<100K |
| tags: |
| - text-classification |
| - safety |
| - guardrail |
| - adversarial |
| - ukrainian |
| - multilingual |
| - llm |
| --- |
| |
| # Ukrainian LLM Safety Dataset |
|
|
| ## Dataset Summary |
|
|
| A national security-focused safety classification dataset for Ukrainian LLM infrastructure, developed in partnership with the Ministry of Digital Transformation of Ukraine. Contains adversarial prompts across a 9-class threat taxonomy with synthetic rationales and contrastive examples for knowledge distillation. |
|
|
| ## Files |
|
|
| | File | Samples | Purpose | |
| |------|---------|---------| |
| | `train.jsonl` | 59,377 | Training corpus with contrastive triplets and rationales (synthetic + anonymized ministry logs) | |
| | `benchmark.jsonl` | 450 | Held-out adversarial benchmark (50 samples/class, human-verified) | |
|
|
| ## Taxonomy (9 classes) |
|
|
| - `cybercrime_hacking` |
| - `disinfo_propaganda` |
| - `fraud_soc_eng` |
| - `llm_compromise` |
| - `nat_sec_opsec` |
| - `privacy_pii` |
| - `resource_abuse` |
| - `safe` |
| - `unsafe_content` |
|
|
| ## Format |
|
|
| ### `train.jsonl` |
|
|
| Each line is a JSON object with contrastive distillation fields: |
|
|
| ```json |
| { |
| "prompt": "User prompt text (the adversarial query)", |
| "tag": "cybercrime_hacking", |
| "reasoning": "Detailed explanation of why this prompt belongs to the category, including manipulation techniques identified", |
| "positive": "Semantically equivalent paraphrase preserving the same safety label", |
| "negative": "A semantically close but safe alternative on the same topic (hard negative)", |
| "model_name": "Teacher model used for augmentation" |
| } |
| ``` |
|
|
| Fields: |
| - `prompt` (string): The original adversarial or benign user query |
| - `tag` (string): Safety category label from the 9-class taxonomy |
| - `reasoning` (string): Teacher-generated natural-language rationale explaining the classification logic |
| - `positive` (string): Paraphrased variant of `prompt` preserving the same `tag` (used for contrastive learning) |
| - `negative` (string): A safe query on the same topic with high lexical overlap but different intent (hard negative for ACD) |
| - `model_name` (string): Identifier of the teacher model that synthesized this sample |
|
|
| ### `benchmark.jsonl` |
|
|
| Each line is a JSON object with minimal fields for evaluation: |
|
|
| ```json |
| { |
| "prompt": "User prompt text", |
| "tag": "llm_compromise" |
| } |
| ``` |
|
|
| Fields: |
| - `prompt` (string): The adversarial query |
| - `tag` (string): Ground-truth safety category label |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("mark-matviiv/ukrainian-safety-dataset") |
| train = dataset["train"] |
| benchmark = dataset["benchmark"] |
| ``` |
|
|
| ## Access |
|
|
| This dataset is gated. Access requests are reviewed by the Ministry of Digital Transformation of Ukraine. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @mastersthesis{matviiv2026guardrail, |
| title={Efficient Guardrailing for the Ukrainian LLM via Reasoning Distillation}, |
| author={Matviiv, Markiian}, |
| year={2026}, |
| school={Ukrainian Catholic University} |
| } |
| ``` |
|
|
