| --- |
| pretty_name: GuidedBench |
| license: cc-by-4.0 |
| version: "1.0" |
| language: |
| - en |
| size_categories: |
| - n<1K |
| task_categories: |
| - text-classification |
| tags: |
| - llm |
| - jailbreak |
| - ai-safety |
| - evaluation |
| extra_gated_heading: Access GuidedBench |
| extra_gated_description: >- |
| Please tell us briefly how you plan to use GuidedBench. This information is |
| collected to manage responsible access to a safety evaluation dataset. |
| extra_gated_button_content: Request access |
| extra_gated_prompt: >- |
| GuidedBench contains harmful prompts and is intended for controlled AI |
| safety evaluation, red teaming, education, and defensive research. |
| extra_gated_fields: |
| Affiliation: text |
| Intended use: |
| type: select |
| options: |
| - Academic research |
| - Education |
| - Safety evaluation |
| - Red teaming |
| - Other |
| Country or region: country |
| I acknowledge the GuidedBench license and harmful content: checkbox |
| configs: |
| - config_name: default |
| data_files: |
| - split: core |
| path: core.jsonl |
| - split: additional |
| path: additional.jsonl |
| --- |
| |
| # GuidedBench |
|
|
| GuidedBench is a guideline-grounded benchmark for evaluating LLM jailbreak |
| methods. Each question is paired with verified, case-specific entity and |
| action guidelines describing the content a successful response should |
| contain. |
|
|
| ## Dataset structure |
|
|
| - `core`: 180 cases from 15 topics that were consistently refused by the |
| victim-model families studied in the paper. |
| - `additional`: 20 cases from five policy-dependent topics, reported |
| separately because vendor policies and baseline refusal behavior differ. |
|
|
| Each record contains: |
|
|
| - `id`: stable identifier such as `guidedbench-000`; |
| - `index`: original integer index; |
| - `benchmark_version`: data version; |
| - `subset`: `core` or `additional`; |
| - `topic`: harmful-topic category; |
| - `question`: benchmark question; |
| - `guidelines`: case-specific scoring points, each with an id, type, |
| description, and examples; |
| - `target`: affirmative target prefix retained from the original release. |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("HRXUST/GuidedBench") |
| print(dataset["core"][0]) |
| ``` |
|
|
| The official evaluator and additional provider backends are available from |
| the [GuidedBench repository](https://github.com/SproutNan/GuidedBench). |
|
|
| ## Safety |
|
|
| The dataset contains harmful objectives and examples. It is intended solely |
| for controlled AI-safety evaluation, red teaming, and defensive research. |
|
|
| ## License |
|
|
| GuidedBench is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). |
| When sharing or adapting the dataset, credit the GuidedBench authors, link to |
| the license, and indicate whether changes were made. See `LICENSE` for details. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{huang2026guidedbench, |
| title = {{GuidedBench}: Measuring and Mitigating the Evaluation Discrepancies of In-the-wild {LLM} Jailbreak Methods}, |
| author = {Ruixuan Huang and Xunguang Wang and Zongjie Li and Daoyuan Wu and Shuai Wang}, |
| booktitle = {International Conference on Learning Representations}, |
| year = {2026}, |
| url = {https://openreview.net/forum?id=ZVg8y3ibyM} |
| } |
| ``` |
|
|
