# RA-QA: A Benchmarking System for Respiratory Audio Question Answering Under Real-World Heterogeneity

arXiv: https://arxiv.org/abs/2602.18452 · HTML: https://mlsanity.com/paper/2602.18452/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 4 Feb 2026.

## Builds on (most cited first, 21 of 21)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Qwen2.5-Omni Technical Report](https://mlsanity.com/paper/2503.20215/) (2025)
- [Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models](https://mlsanity.com/paper/2311.07919/) (2023)
- [Pengi: An Audio Language Model for Audio Tasks](https://mlsanity.com/paper/2305.11834/) (2023)
- [Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models](https://mlsanity.com/paper/2507.08128/) (2025)
- [OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM](https://mlsanity.com/paper/2402.09181/) (2024)
- [emrQA: A Large Corpus for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1809.00732/) (2018)
- [Text-to-SQL Generation for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1908.01839/) (2019)
- [SpeechVerse: A Large-scale Generalizable Audio Language Model](https://mlsanity.com/paper/2405.08295/) (2024)
- [The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms](https://mlsanity.com/paper/2009.11644/) (2020)
- [ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram](https://mlsanity.com/paper/2306.15681/) (2023)
- [EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records](https://mlsanity.com/paper/2301.07695/) (2023)
- [Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking](https://mlsanity.com/paper/2406.16148/) (2024)
- [Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection](https://mlsanity.com/paper/2305.12741/) (2023)
- [Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification](https://mlsanity.com/paper/2105.07566/) (2021)
- [Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers](https://mlsanity.com/paper/2212.08570/) (2022)
- [RespLLM: Unifying Audio and Text with Multimodal LLMs for Generalized Respiratory Health Prediction](https://mlsanity.com/paper/2410.05361/) (2024)
- [CaReAQA: A Cardiac and Respiratory Audio Question Answering Model for Open-Ended Diagnostic Reasoning](https://mlsanity.com/paper/2505.01199/) (2025)
- [Multimedia Respiratory Database (RespiratoryDatabase@TR): Auscultation Sounds and Chest X-rays](https://mlsanity.com/paper/2101.10946/) (2021)
- [A Snoring Sound Dataset for Body Position Recognition: Collection, Annotation, and Analysis](https://mlsanity.com/paper/2307.13346/) (2023)

## Abstract (arXiv)

As conversational multimodal AI tools are increasingly adopted to process patient data for health assessment, robust benchmarks are needed to measure progress and expose failure modes under realistic conditions. Despite the importance of respiratory audio for mobile health screening, respiratory audio question answering remains underexplored, with existing studies evaluated narrowly and lacking real-world heterogeneity across modalities, devices, and question types. We hence introduce the Respiratory-Audio Question-Answering (RA-QA) benchmark, including a standardized data generation pipeline, a comprehensive multimodal QA collection, and a unified evaluation protocol. RA-QA harmonizes public RA datasets into a collection of 9 million format-diverse QA pairs covering diagnostic and contextual attributes. We benchmark general audio-language models as well as domain-specific architectures, establishing reproducible reference points and showing how current approaches fail under heterogeneity.