mucAI at AraHealthQA 2025: Explain–Retrieve–Verify (ERV) Workflow for Multi-Label Arabic Health QA Classification
Year: 2025
Type: conference-paper
Abstract: We present a simple, training-light pipeline for multi-label categorization of Arabic mental-health questions in the AraHealthQA 2025 MentalQA Track 1 (question and answer classification).Our method, Explain-Retrieve-Verify (ERV), couples a chainof-thought LLM classifier with example-based retrieval and a verifier that arbitrates disagreements.The LLM first proposes candidate labels and rationales from a compact taxonomy prompt.A similarity agent then surfaces topk nearest questions via multilingual sentencetransformer embeddings to induce case-based priors.A verification agent reconciles both signals to produce a final label set with a calibrated confidence, followed by a lightweight post-processor for code parsing and confidence clamping.ERV requires no fine-tuning or external data and runs efficiently at inference time.In shared-task evaluation, our system achieved 0.61 weighted F1-score for question classification and 0.73 for answer classification.A hybrid approach combining ERV with MAR-BERT further improves answer classification to 0.80 weighted F1-score.
Author: Ahmed Abdou
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416037805