| --- |
| license: odc-by |
| configs: |
| - config_name: de |
| data_files: |
| - split: train |
| path: de/train-* |
| - config_name: en |
| data_files: |
| - split: train |
| path: en/train-* |
| - config_name: es |
| data_files: |
| - split: train |
| path: es/train-* |
| - config_name: fr |
| data_files: |
| - split: train |
| path: fr/train-* |
| - config_name: it |
| data_files: |
| - split: train |
| path: it/train-* |
| dataset_info: |
| - config_name: de |
| features: |
| - name: lang |
| dtype: large_string |
| - name: prompt |
| dtype: large_string |
| - name: target |
| dtype: large_string |
| splits: |
| - name: train |
| num_bytes: 81753 |
| num_examples: 104 |
| download_size: 41828 |
| dataset_size: 81753 |
| - config_name: en |
| features: |
| - name: lang |
| dtype: large_string |
| - name: prompt |
| dtype: large_string |
| - name: target |
| dtype: large_string |
| splits: |
| - name: train |
| num_bytes: 103762 |
| num_examples: 104 |
| download_size: 55135 |
| dataset_size: 103762 |
| - config_name: es |
| features: |
| - name: lang |
| dtype: large_string |
| - name: prompt |
| dtype: large_string |
| - name: target |
| dtype: large_string |
| splits: |
| - name: train |
| num_bytes: 96316 |
| num_examples: 104 |
| download_size: 46976 |
| dataset_size: 96316 |
| - config_name: fr |
| features: |
| - name: lang |
| dtype: large_string |
| - name: prompt |
| dtype: large_string |
| - name: target |
| dtype: large_string |
| splits: |
| - name: train |
| num_bytes: 122878 |
| num_examples: 104 |
| download_size: 59609 |
| dataset_size: 122878 |
| - config_name: it |
| features: |
| - name: lang |
| dtype: large_string |
| - name: prompt |
| dtype: large_string |
| - name: target |
| dtype: large_string |
| splits: |
| - name: train |
| num_bytes: 95849 |
| num_examples: 104 |
| download_size: 50635 |
| dataset_size: 95849 |
| |
| --- |
| |
| # Multi-AdvBench |
|
|
| ## Overview |
|
|
| **Multi-AdvBench** is a **multilingual adversarial benchmark dataset** derived from the original [multilingual_advbench](https://huggingface.co/datasets/simonycl/multilingual_advbench) dataset from [simonycl](https://huggingface.co/simonycl). |
|
|
| The original multilingual_advbench dataset consists of **prompt–target pairs**, where: |
| - the **prompt** is a harmful request, |
| - the **target** is an unintended (unsafe) response. |
| |
| The new dataset preserves the original prompts and **re-generates the targets** using a **permissively licensed** multimodal model: **Qwen3-VL-235B-A22B-Instruct-FP8** |
| |
| As a result, Multi-AdvBench provides a multilingual dataset using the original prompts while ensuring that all generated responses are **fully safe**. |
| The new dataset therefore consists of **prompt–target pairs**, where: |
| - the **prompt** is a harmful request, |
| - the **target** is an expected safe response. |
| |
| --- |
| |
| ## Sets processing, target generation and language distribution |
| |
| The original dataset contains 520 prompt–target pairs. |
| The same 520 pairs are available in each language and in the same order. |
| |
| Our process was as follows: |
| - we selected **five languages**, |
| - we discarded the targets from each prompt–target pair, |
| - we created a global list of indices from 0 to 519 and randomly shuffled it, |
| - we split this list into five disjoint blocks of 104 indices, one block per language, |
| - for each language, we selected only the prompts at the indices assigned to that language. |
| As a result: |
| |
| - each language contains exactly **104 prompt–target pairs** |
| - no prompt is shared between languages |
| - all 520 prompts are used exactly once |
| - for each prompt we generated a new **safe response**, |
| - **every prompt-target pair has been manually reviewed**. |
| |
| | Language | Percentage | Number of Examples | |
| |----------|------------|--------------------| |
| | English | 20% | 104 | |
| | Italian | 20% | 104 | |
| | French | 20% | 104 | |
| | Spanish | 20% | 104 | |
| | German | 20% | 104 | |
| |
| --- |
| |
| ## Intended Use |
| |
| This dataset provides **multilingual adversarial benchmark prompt–target pairs** suitable for: |
| |
| - training LLMs for guardrailing purposes, |
| - benchmarking model performance across languages, |
| - developing new models requiring **permissively licensed** training data. |
| |
| Because responses were generated using a permissively licensed model, all data can be **freely reused for research and commercial model development**. |
| |
| --- |
| |
| ## Dataset Structure |
| |
| We maintained the choice made by the authors of the original dataset to name the fields "prompt" and "target". |
| Each example in every language contains the following fields: |
| |
| | Field | Type | Description | |
| | ------- | ------ | ------------- | |
| | `lang` | string | the language of the prompt–target pair | |
| | `prompt` | string | a harmful prompt | |
| | `target` | string | a safe response to the prompt | |
| |
| --- |
| |
| ## Licensing |
| |
| The dataset is distributed under the **ODC-BY-1.0** license. |
| Users must comply with all attribution and usage requirements specified by the license. |
| |
