Summary of Welldunn: on the Robustness and Explainability Of Language Models and Large Language Models in Identifying Wellness Dimensions, by Seyedali Mohammadi et al.
WellDunn: On the Robustness and Explainability of Language Models and Large Language Models in Identifying Wellness Dimensions
by Seyedali Mohammadi, Edward Raff, Jinendra Malekar, Vedant Palit, Francis Ferraro, Manas Gaur
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 use of Language Models (LMs) in mental health applications, where predictive performance may not be sufficient to ensure a model’s utility in clinical practice. The authors examine the attention fidelity of LMs, introducing an evaluation design that focuses on their robustness and explainability in identifying Wellness Dimensions (WDs). They analyze two existing datasets: Multi-label Classification-based MultiWD and WellXplain, using Halbert Dunn’s theory of wellness as grounding for the labels. The results show that despite their capabilities, GPT-3.5/4 lags behind RoBERTa, and fine-tuned LLMs on WellXplain fail to improve performance or explanations. The study highlights the need for further research into the consistency and explanations of mental health-specific LMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well language models can help with mental health issues. It’s not just about being good at guessing what someone might say, but also making sure the model explains its answers in a way that makes sense to experts. The authors test different models on two big datasets and find some surprising things – for example, some models are really bad at explaining themselves! They think this is important because we need to make sure language models can be trusted when it comes to helping people with mental health issues. |
Keywords
» Artificial intelligence » Attention » Classification » Gpt » Grounding
