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

TL;DR

FirstAidQA is a synthetic dataset of 5,500 high-quality question-answer pairs created using ChatGPT-4o-mini, aimed at enabling lightweight, offline-capable AI models for first aid and emergency response in low-connectivity environments.

Contribution

The paper introduces FirstAidQA, a novel high-quality synthetic dataset specifically designed for training AI models in emergency and first aid scenarios, addressing a critical data gap.

Findings

Dataset supports instruction-tuning of LLMs and SLMs.

Enables development of offline, resource-efficient emergency response AI.

Publicly available for research and development.

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.