Summary of Zero-shot End-to-end Spoken Question Answering in Medical Domain, by Yanis Labrak et al.
Zero-Shot End-To-End Spoken Question Answering In Medical Domain
by Yanis Labrak, Adel Moumen, Richard Dufour, Mickael Rouvier
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the integration of large language models (LLMs) for spoken question-answering (SQA) in the medical domain, introducing a novel zero-shot approach compared to traditional cascade systems. The study conducts a comprehensive evaluation on an open benchmark of 8 medical tasks and 48 hours of synthetic audio, demonstrating that the E2E methodology requires up to 14.7 times fewer resources while improving average accuracy by 0.5%. This showcases the potential of E2E methodologies for SQA in resource-constrained contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using big language models to answer medical questions from audio recordings. It’s trying to find a better way to do this that uses one model instead of two, and it works really well! The researchers tested their idea on lots of medical questions and showed that it needs fewer resources and gives more accurate answers. |
Keywords
» Artificial intelligence » Question answering » Zero shot
