FirstAidQA: A Synthetic Dataset for First Aid and Emergency Response in Low-Connectivity Settings
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912683663556608 |
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| author | Muna, Saiyma Sittul Salvi, Rezwan Islam Mushfique, Mushfiqur Rahman Abrar, Ajwad |
| author_facet | Muna, Saiyma Sittul Salvi, Rezwan Islam Mushfique, Mushfiqur Rahman Abrar, Ajwad |
| contents | In emergency situations, every second counts. The deployment of Large Language Models (LLMs) in time-sensitive, low or zero-connectivity environments remains limited. Current models are computationally intensive and unsuitable for low-tier devices often used by first responders or civilians. A major barrier to developing lightweight, domain-specific solutions is the lack of high-quality datasets tailored to first aid and emergency response. To address this gap, we introduce FirstAidQA, a synthetic dataset containing 5,500 high-quality question answer pairs that encompass a wide range of first aid and emergency response scenarios. The dataset was generated using a Large Language Model, ChatGPT-4o-mini, with prompt-based in-context learning, using texts from the Vital First Aid Book (2019). We applied preprocessing steps such as text cleaning, contextual chunking, and filtering, followed by human validation to ensure accuracy, safety, and practical relevance of the QA pairs. FirstAidQA is designed to support instruction-tuning and fine-tuning of LLMs and Small Language Models (SLMs), enabling faster, more reliable, and offline-capable systems for emergency settings. We publicly release the dataset to advance research on safety-critical and resource-constrained AI applications in first aid and emergency response. The dataset is available on Hugging Face at https://huggingface.co/datasets/i-am-mushfiq/FirstAidQA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01289 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FirstAidQA: A Synthetic Dataset for First Aid and Emergency Response in Low-Connectivity Settings Muna, Saiyma Sittul Salvi, Rezwan Islam Mushfique, Mushfiqur Rahman Abrar, Ajwad Computation and Language In emergency situations, every second counts. The deployment of Large Language Models (LLMs) in time-sensitive, low or zero-connectivity environments remains limited. Current models are computationally intensive and unsuitable for low-tier devices often used by first responders or civilians. A major barrier to developing lightweight, domain-specific solutions is the lack of high-quality datasets tailored to first aid and emergency response. To address this gap, we introduce FirstAidQA, a synthetic dataset containing 5,500 high-quality question answer pairs that encompass a wide range of first aid and emergency response scenarios. The dataset was generated using a Large Language Model, ChatGPT-4o-mini, with prompt-based in-context learning, using texts from the Vital First Aid Book (2019). We applied preprocessing steps such as text cleaning, contextual chunking, and filtering, followed by human validation to ensure accuracy, safety, and practical relevance of the QA pairs. FirstAidQA is designed to support instruction-tuning and fine-tuning of LLMs and Small Language Models (SLMs), enabling faster, more reliable, and offline-capable systems for emergency settings. We publicly release the dataset to advance research on safety-critical and resource-constrained AI applications in first aid and emergency response. The dataset is available on Hugging Face at https://huggingface.co/datasets/i-am-mushfiq/FirstAidQA. |
| title | FirstAidQA: A Synthetic Dataset for First Aid and Emergency Response in Low-Connectivity Settings |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2511.01289 |