| --- |
| 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: id |
| dtype: string |
| - name: instruction |
| dtype: string |
| - name: input_persona |
| dtype: string |
| - name: response |
| dtype: string |
| - name: given_constraints |
| list: string |
| splits: |
| - name: train |
| num_bytes: 21188619 |
| num_examples: 15000 |
| download_size: 11905272 |
| dataset_size: 21188619 |
| - config_name: en |
| features: |
| - name: id |
| dtype: string |
| - name: instruction |
| dtype: string |
| - name: input_persona |
| dtype: string |
| - name: response |
| dtype: string |
| - name: given_constraints |
| list: string |
| splits: |
| - name: train |
| num_bytes: 21118503 |
| num_examples: 15000 |
| download_size: 12320551 |
| dataset_size: 21118503 |
| - config_name: es |
| features: |
| - name: id |
| dtype: string |
| - name: instruction |
| dtype: string |
| - name: input_persona |
| dtype: string |
| - name: response |
| dtype: string |
| - name: given_constraints |
| list: string |
| splits: |
| - name: train |
| num_bytes: 21775902 |
| num_examples: 15000 |
| download_size: 12062233 |
| dataset_size: 21775902 |
| - config_name: fr |
| features: |
| - name: id |
| dtype: string |
| - name: instruction |
| dtype: string |
| - name: input_persona |
| dtype: string |
| - name: response |
| dtype: string |
| - name: given_constraints |
| list: string |
| splits: |
| - name: train |
| num_bytes: 22439150 |
| num_examples: 15000 |
| download_size: 12644042 |
| dataset_size: 22439150 |
| - config_name: it |
| features: |
| - name: id |
| dtype: string |
| - name: instruction |
| dtype: string |
| - name: input_persona |
| dtype: string |
| - name: response |
| dtype: string |
| - name: given_constraints |
| list: string |
| splits: |
| - name: train |
| num_bytes: 20892888 |
| num_examples: 15000 |
| download_size: 11935183 |
| dataset_size: 20892888 |
| language: |
| - de |
| - en |
| - it |
| - es |
| - fr |
| size_categories: |
| - 10K<n<100K |
| --- |
| # Multilingual Persona IF Dataset |
|
|
| ## Overview |
|
|
| **Multilingual Persona IF** is a **multilingual** version of the English dataset [Persona IF](https://huggingface.co/datasets/allenai/tulu-3-sft-personas-instruction-following), create by AllenAI as part of the Tulu3 dataset suite. The original Persona IF dataset is also a synthetic dataset, created for instruction-tuning of Large Language Models. It has a particular focus on satisfying **constraints** imposed by the user in the request to the assistant, and as such borrows a list of constraints from the benchmarking dataset [IFEval](https://huggingface.co/datasets/google/IFEval). |
|
|
| The pipeline for generating the dataset is described in the AllenAI [paper](https://arxiv.org/pdf/2411.15124) about their open model Tulu3. Their code can be found [in this repository](https://github.com/allenai/open-instruct). They started from a list of constraints, then manually wrote some **seed instructions** for each of the constraints. They then prompted a LLM to generate an instruction containing 1-3 randomly chosen constraints, using the seeds as few-shot examples. Once the instructions were generated, they asked the same LLM to also provide the assistant's responses to the instructions. |
|
|
| In order to create some variety in the instructions, they used the [Persona Hub](https://huggingface.co/datasets/proj-persona/PersonaHub) personas by [Ge et al., 2024](https://arxiv.org/pdf/2406.20094). When prompting the LLM, they asked to generate an instruction that the given persona might ask an assistant to do. |
|
|
| To generate the multilingual version of Persona IF, we employed the same methodology and pipeline as AllenAI, but we used the permissively licensed LLM **Qwen3.5-122B-A10B** and VillanovaAI's multilingual Persona [dataset](https://huggingface.co/datasets/VillanovaAI/multi-persona-hub-unique). We also reworked and translated the list of constraints, seed instructions and prompts to fit our needs. |
|
|
| ## Language Distribution |
|
|
| The original dataset contains 29,980 English instructions. Since we wouldn't be translating the original dataset, we chose to generate 15,000 instructions for each of our five languages for a total of 75,000 instructions. The final distribution is as follows: |
|
|
| | Language | Subset Name | Percentage | Number of Examples | |
| |----------|------------ |------------|--------------------| |
| | English | en | 20% | 15,000 | |
| | Italian | it | 20% | 15,000 | |
| | French | fr | 20% | 15,000 | |
| | Spanish | es | 20% | 15,000 | |
| | German | de | 20% | 15,000 | |
|
|
| ## Additional information |
|
|
| When we examined the original list of constraints, we noticed that some constraints were logically **incompatible** with each other and would result in an instruction that would be effectively impossible to follow. An example of this situation are the two constraint types "in lowercase" and "number of capital words": it is impossible to write a text in lowercase that containts a number of capital words other than 0. Therefore we modified the randomized constraints sampling pipeline to avoid such conflicts. |
|
|
| ### Dataset Structure |
|
|
| Each entry contains the following **fields**: |
|
|
| | Field | Type | Description | |
| | ------- | ------ | ------------- | |
| | `id` | string | Unique index of the instruction | |
| | `instruction` | string | Text of the generated instruction | |
| | `input_persona` | string | The persona used to prompt the LLM when generating the instruction | |
| | `response` | string | Text of the assistant's response | |
| | `given_constraints` | list | A list of 1 to 3 randomly chosen constraints to be followed | |
|
|
| ## Licensing |
|
|
| The dataset is distributed under the **ODC-BY-1.0** license. |
| Users must adhere to the attribution and usage requirements outlined under this license. |
|
|
