FirstAidQA: A Synthetic Dataset for First Aid and Emergency Response in Low-Connectivity Settings

TL;DR

This work addresses the lack of domain-specific QA data for first aid and emergency response in low-connectivity settings. It introduces FirstAidQA, a synthetic dataset of 5,500 QA pairs generated via in-context prompting of ChatGPT-4o-mini using content from the Vital First Aid Book 2019, with subsequent cleaning and human validation. The authors implement a pipeline including source material selection, a structured task taxonomy, and prompting strategies to produce diverse, practical, and safety-conscious Q&As. They couple filtering, safety checks, and expert review to ensure accuracy and reduce unsafe guidance, reporting mean scores across several quality criteria. The dataset is released publicly to enable instruction-tuning and offline deployment of lightweight models for real-time emergency guidance, with potential impact for disaster zones and rural settings.

Abstract

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.