Summary of Respllm: Unifying Audio and Text with Multimodal Llms For Generalized Respiratory Health Prediction, by Yuwei Zhang et al.
RespLLM: Unifying Audio and Text with Multimodal LLMs for Generalized Respiratory Health Prediction
by Yuwei Zhang, Tong Xia, Aaqib Saeed, Cecilia Mascolo
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes a novel machine learning framework called RespLLM for predicting respiratory health based on multimodal data including demographics, medical history, symptoms, and audio recordings of lung sounds. The existing approaches are insufficient and lack generalizability due to limited training data, basic fusion techniques, and task-specific models. The proposed RespLLM framework leverages the extensive prior knowledge of large language models (LLMs) and enables effective audio-text fusion through cross-modal attentions. It also employs instruction tuning to integrate diverse data from multiple sources, ensuring generalizability and versatility of the model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RespLLM is a new way to use computers to help doctors diagnose respiratory diseases like asthma or chronic obstructive pulmonary disease (COPD). This technology can listen to audio recordings of lung sounds and read information about people’s health to make predictions. It’s better than other approaches because it uses big datasets and clever ways to combine text and audio data. |
Keywords
» Artificial intelligence » Instruction tuning » Machine learning
