| --- |
| language: |
| - en |
| - hi |
| - fr |
| - de |
| - es |
| - it |
| - ja |
| - zh |
| task_categories: |
| - text-classification |
| - text-generation |
| --- |
| |
| # Assistive Prompting Disabilities Dataset |
|
|
| ## Dataset Description |
|
|
| This dataset contains prompts designed to assist individuals with disabilities in generating helpful text using AI systems. The dataset includes prompts across multiple languages and different levels of textual noise. |
|
|
| Each prompt is provided in a clean form as well as multiple noisy variations. |
|
|
| ## Dataset Structure |
|
|
| The dataset contains the following columns: |
|
|
| - **Clean_text** – The original prompt without noise |
| - **Category** – Category of the prompt (e.g., communication, task delegation) |
| - **alpha_0.2** – Prompt with low noise |
| - **alpha_0.4** – Prompt with moderate noise |
| - **alpha_0.6** – Prompt with higher noise |
| - **alpha_0.8** – Prompt with heavy noise |
| - **Noise type** – Type of noise applied (N1, N2, N3, N4) |
| |
| ## Languages |
| |
| The dataset includes prompts in multiple languages including: |
| |
| - English |
| - Hindi |
| - Chinese |
| - French |
| - German |
| - Spanish |
| - Italian |
| - Japanese |
| |
| ## Noise types |
| |
| The dataset includes the following noise variants to evaluate model robustness under different types of input perturbations. |
| |
| N1 – Typographical Noise: Introduces spelling errors or minor typographical variations in the text. |
| |
| N2 – Lexical Noise: Replaces words with semantically similar alternatives while preserving the original meaning. |
| |
| N3 – Structural Noise: Modifies the sentence structure or word order without altering the underlying intent. |
| |
| N4 – Extraneous Noise: Adds additional irrelevant words or phrases that do not contribute to the core meaning of the prompt. |
| |
| ## Dataset Size |
| |
| - Total samples: **10,232** |
| |
