Summary of Understanding Student Sentiment on Mental Health Support in Colleges Using Large Language Models, by Palak Sood et al.
Understanding Student Sentiment on Mental Health Support in Colleges Using Large Language Models
by Palak Sood, Chengyang He, Divyanshu Gupta, Yue Ning, Ping Wang
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 tackles the challenge of evaluating mental health support in colleges using advanced machine learning methods. Traditional approaches rely on qualitative analysis, which can be limited by human bias. This study uses large language models (LLMs) to analyze student sentiments on mental health support from public Student Voice Survey data. A new dataset, SMILE-College, was created through human-machine collaboration. The investigation compared traditional machine learning methods with state-of-the-art LLMs, finding that GPT-3.5 and BERT performed best on this dataset. The results highlight the difficulties in accurately predicting response sentiments and offer practical insights into how LLMs can enhance mental health-related research and improve college mental health services. By leveraging LLMs, researchers can facilitate efficient and informed mental health support evaluation, management, and decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers are trying to figure out if colleges are doing a good job supporting students’ mental health. They wanted to find a way to measure this that’s fair and reliable. Right now, they’re relying on people’s opinions, which can be biased. This study uses special computer programs called large language models (LLMs) to understand what college students think about their mental health support. The team created a new dataset by working with both humans and computers. They compared different ways of analyzing the data and found that some LLMs worked better than others. The results show that accurately predicting how students feel is tricky, but it can help make college mental health services better. By using these special computer programs, researchers can make more informed decisions to support students’ mental health. |
Keywords
» Artificial intelligence » Bert » Gpt » Machine learning
