license: cc-by-4.0
language:
- ar
tags:
- medical
- arabic
- first-aid
- qa
- healthcare
- emergency
- low-resource-language
- dataset
- dialect
- Msa
pretty_name: FALAH-Mix
task_categories:
- text-classification
- question-answering
size_categories:
- 1K<n<10K
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Dataset Details
Dataset Description
FALAH (First-Aid Lifesaving Arabic QA Dataset for Help in Emergency Situations) is a high-quality, expert-validated Arabic dataset dedicated to first-aid question answering in Modern Standard Arabic (MSA). The dataset consists of 104 first-aid question–answer (QA) pairs, carefully extracted and filtered from large-scale Arabic medical QA datasets, then manually annotated by medical professionals to ensure clinical relevance and correctness.
FALAH was created to address the scarcity of Arabic first-aid conversational resources and to support the development of specialized Arabic first-aid chatbots.
- Curated by: Imane MABROUK (INSEA – National Institute of Statistics and Applied Economics, Rabat, Morocco)
- Supervised by: Dr. Rana R. Malhas (bigIR Research Group, Qatar University) & Dr. Imane Chlioui (INSEA)
- Domain: First Aid / Emergency Care
- Language(s) (NLP): Modern Standard Arabic (MSA), Arabic Dialect (AD)
- License: CC BY 4.0
- Funded by: Self-funded academic project (Academic graduation project)
The dataset was built as part of the PFE project titled:
- Towards Building an Arabic First-Aid Chatbot using FA-AraBERT Classifier and FALAH Dataset
Dataset Sources [optional]
The FALAH dataset was constructed from the following publicly available Arabic medical QA datasets:
- AHD(Arabic Healthcare Dataset): H. Gawbah, A. Alsubari, and N. A. Al-Majmar, “AHD: Arabic healthcare dataset,” Sept. 2024.
- MAQA(Medical Arabic QA Dataset): M. Abdelhay, A. Mohammed, and H. A. Hefny, “Deep learning for Arabic healthcare: MedicalBot,” Social Network Analysis and Mining, vol. 13, p. 71, Apr. 2023.
Additional inspiration and keyword guidance were derived from:
- Mayo Clinic First-Aid QA Dataset: Jomana Anwar, Peter Nadi, and Noha Seddik, “Towards Building a Chatbot-Based First Aid Service in Arabic Language,” Journal of Advanced Research in Applied Sciences and Engineering Technology, vol. 45, pp. 1–10, May 2024.
Uses
Direct Use
FALAH dataset is intended for:
- Arabic first-aid question answering systems
- First-aid chatbot development
- Emergency-care NLP research in Arabic
- Few-shot prompting experiments for LLMs
- Evaluation of Arabic LLMs in emergency medical scenarios
The dataset was used in the project to:
- Provide few-shot examples for LLM prompting
- Evaluate Arabic and multilingual LLMs using BERTScore
Out-of-Scope Use
FALAH dataset should NOT be used:
As a substitute for professional medical consultation
For real-time clinical decision-making without human supervision
In high-risk healthcare applications without expert validation
For non-medical NLP tasks
This dataset is intended strictly for research and experimental development.
Dataset Structure
The dataset consists of 104 QA pairs.
Characteristics:
Focused exclusively on first-aid and emergency scenarios
Covers QA pairs from different categories realted to first-aid
Expert-validated for medical relevance
Designed for conversational AI applications
In the broader project, FALAH dataset was integrated into FALAH-Mix dataset(FALAH dataset with non first-aid QA pairs) for classification tasks and used independently for few-shot prompting.
Dataset Creation
Curation Rationale
Despite the existence of large Arabic medical QA datasets, very few resources specifically focus on first-aid scenarios.
For example:
AHD contained only 68 explicitly labeled first-aid QA pairs.
Mayo Clinic First-Aid dataset contains 374 QA pairs.
This scarcity motivated the creation of FALAH — a curated Arabic first-aid dataset derived from real Arabic medical consultations.
The goal was to create a clinically relevant dataset to support emergency-aware Arabic chatbot systems, particularly useful in humanitarian and crisis contexts.
Source Data
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