Summary of Sentiment Reasoning For Healthcare, by Khai-nguyen Nguyen et al.
Sentiment Reasoning for Healthcare
by Khai-Nguyen Nguyen, Khai Le-Duc, Bach Phan Tat, Duy Le, Long Vo-Dang, Truong-Son Hy
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 In this research, the authors focus on improving transparency in AI healthcare decision-making by developing a new task called Sentiment Reasoning. This task involves predicting both the sentiment label and generating a rationale for the input transcript, allowing Large Language Models (LLMs) to understand emotions in context and handle nuanced language. The proposed multimodal multitask framework and dataset demonstrate that Sentiment Reasoning can improve model transparency by providing rationales for model predictions, with quality comparable to humans. Additionally, fine-tuning models with rationales leads to improved performance, with a 1% increase in accuracy and macro-F1. The study uses both human transcripts and Automatic Speech Recognition (ASR) transcripts, showing no significant difference in the semantic quality of generated rationales between the two. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers try to make AI decision-making more transparent in healthcare by teaching machines to understand emotions and reasons behind their predictions. They create a new task called Sentiment Reasoning, where models predict how someone feels and why they feel that way. This helps improve model performance and makes it easier for humans to trust AI decisions. |
Keywords
» Artificial intelligence » Fine tuning
