WellDunn: On the Robustness and Explainability of Language Models and Large Language Models in Identifying Wellness Dimensions
Summary
Paper digest
What problem does the paper attempt to solve? Is this a new problem?
The paper aims to address the need for robustness and explainability of Language Models (LMs) and Large Language Models (LLMs) in identifying Wellness Dimensions (WD) for mental health applications . This paper focuses on evaluating the attention fidelity of these models and their alignment with ground truth explanations, highlighting disparities between prediction accuracy and attention . While previous studies have concentrated on developing machine learning algorithms for mental health conditions, minimal attention has been given to ensuring the robustness and explanatory capabilities of AI-driven models in this context . This paper's emphasis on the explainability and reliability of predictions generated by LMs and LLMs in mental health applications is a new and critical problem in the field .
What scientific hypothesis does this paper seek to validate?
This paper seeks to validate the scientific hypothesis related to the robustness and explainability of Language Models (LMs) and Large Language Models (LLMs) in identifying Wellness Dimensions (WD) . The study focuses on evaluating the attention fidelity of these models and their impact on ground truth explanations, particularly in the context of mental health applications . The research aims to highlight disparities between prediction accuracy and attention, emphasizing the importance of a transparent classifier rooted in clinical understanding .
What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?
The paper "WellDunn: On the Robustness and Explainability of Language Models and Large Language Models in Identifying Wellness Dimensions" introduces several novel ideas, methods, and models in the field of mental health applications using Language Models (LMs) and Large Language Models (LLMs) .
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Evaluation Design: The paper focuses on the robustness and explainability of LMs in identifying Wellness Dimensions (WD) by evaluating two mental health and well-being datasets: MULTIWD and WELLXPLAIN . The evaluation design includes fine-tuning general-purpose and domain-specific LMs, followed by feeding them into a feed-forward neural network classifier. The paper utilizes Sigmoid Cross Entropy (SCE) and Gambler’s Loss (GL) as loss functions to assess LMs' robustness, and Singular Value Decomposition (SVD) and Attention-Overlap (AO) Score to assess explainability .
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Surprising Results: The study reveals several surprising results about LMs and LLMs. Despite their human-like capabilities, GPT-3.5/4 lag behind RoBERTa, and MEDALPACA, a fine-tuned LLM, fails to deliver remarkable improvements in performance or explanations. The alignment between attention and explanations remains low across all LMs/LLMs, with LLMs scoring a dismal 0.0 .
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Application to Mental Health Domains: The paper discusses the potential application of the WellDunn benchmark to various mental health topics beyond depression, anxiety, bipolar disorder, schizophrenia, and suicide risk. It highlights that the underlying framework and methodology of the benchmark have the potential to be applied to a variety of other mental health topics, emphasizing the intersection of causal factors and wellness dimensions across different mental health conditions .
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Dataset Considerations: The study addresses the limitations of prior research on mental health information, which mainly focused on text data. It emphasizes the importance of incorporating multiple modalities of information in mental health datasets and highlights the need for knowledge-grounded and expert-curated datasets that incorporate clinical expertise to improve the generalization of LMs effectively in mental health applications .
Overall, the paper proposes a comprehensive evaluation framework, highlights the disparities in prediction accuracy and attention, and emphasizes the importance of transparent classifiers rooted in clinical understanding for effective mental health applications using LMs and LLMs. The paper "WellDunn: On the Robustness and Explainability of Language Models and Large Language Models in Identifying Wellness Dimensions" introduces novel characteristics and advantages compared to previous methods in mental health applications using Language Models (LMs) and Large Language Models (LLMs) .
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Evaluation Design: The paper's evaluation design focuses on the robustness and explainability of LMs in identifying Wellness Dimensions (WD) by utilizing two mental health datasets: MULTIWD and WELLXPLAIN. It employs fine-tuning general-purpose and domain-specific LMs, followed by a feed-forward neural network classifier. The study incorporates loss functions like Sigmoid Cross Entropy (SCE) and Gambler’s Loss (GL) to assess LMs' robustness and Singular Value Decomposition (SVD) and Attention-Overlap (AO) Score to evaluate explainability .
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Model Performance: The research reveals surprising results about LMs and LLMs, highlighting disparities in prediction accuracy and attention. Despite human-like capabilities, GPT-3.5/4 lags behind RoBERTa, and MEDALPACA, a fine-tuned LLM, does not significantly enhance performance or explanations. The alignment between attention and explanations remains low across all LMs/LLMs, with LLMs scoring poorly at 0.0 .
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Application to Mental Health Domains: The WellDunn benchmark, although currently focusing on specific mental health topics like depression, anxiety, bipolar disorder, schizophrenia, and suicide risk, has the potential to be applied to various other mental health topics. The paper emphasizes the intersection of causal factors and wellness dimensions across different mental health conditions, indicating adaptability to diverse mental health domains without significant deviations in outcomes .
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Dataset Considerations: The study addresses the limitations of prior mental health research that mainly focused on text data. It stresses the importance of incorporating multiple modalities of information in mental health datasets and the need for expert-curated datasets to enhance the generalization of LMs effectively in mental health applications .
Overall, the paper's innovative evaluation framework, insights into model performance, adaptability to various mental health domains, and emphasis on diverse dataset considerations distinguish it from previous methods in mental health applications using LMs and LLMs.
Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?
Several related research studies exist in the field of mental health applications and language models. Noteworthy researchers in this area include Seyedali Mohammadi, Edward Raff, Jinendra Malekar, Vedant Palit, Francis Ferraro, Manas Gaur, and Amit Sheth . These researchers have contributed to studies focusing on the robustness and explainability of language models in identifying wellness dimensions.
The key solution mentioned in the paper revolves around the evaluation design that emphasizes the robustness and explainability of Language Models (LMs) in identifying Wellness Dimensions (WD) in mental health and well-being datasets . The study highlights disparities between prediction accuracy and attention, underscoring the importance of having a transparent classifier rooted in clinical understanding. Additionally, the research reveals surprising results about the performance of different language models in this context, emphasizing the need for alignment between attention and explanations for trustworthy model predictions .
How were the experiments in the paper designed?
The experiments in the paper were designed to focus on the robustness and explainability of Language Models (LMs) in identifying Wellness Dimensions (WD) . The study introduced a pair of datasets for the AI for Social Impact community working on mental health, specifically focusing on Multi-label Classification-based MULTIWD and WELLXPLAIN datasets . The evaluation design aimed to highlight the disparities between prediction accuracy and attention, emphasizing the need for a transparent classifier rooted in clinical understanding . The experiments involved benchmarking on domain-specific and general-purpose LMs to analyze prediction accuracy and attention alignment, revealing significant findings about the performance of different LMs and the alignment between attention and explanations . The study also explored the impact of fine-tuning LLMs for medical data and compared different strategies such as zero-shot and few-shot prompting to evaluate LLM performance .
What is the dataset used for quantitative evaluation? Is the code open source?
The dataset used for quantitative evaluation in the study is the WellDunn dataset . The code for the models LLAMA and MEDALPACA is open source, as mentioned in the document .
Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.
The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study focused on evaluating the robustness and explainability of Language Models (LMs) and Large Language Models (LLMs) in identifying Wellness Dimensions (WD) related to mental health . The findings highlighted several key insights:
- The study revealed disparities between prediction accuracy and attention across domain-specific and general-purpose LMs, emphasizing the necessity for transparent classifiers rooted in clinical understanding .
- Despite the advanced capabilities of models like GPT-3.5 and GPT-4, they underperformed compared to models like RoBERTa, and fine-tuned LLMs did not show significant improvements in performance or explanations .
- The alignment between attention and explanations remained low across all LMs/LLMs, with LLMs scoring poorly in this aspect .
- The study also examined the impact of confidence-oriented loss functions on prediction performance, revealing a significant drop in performance for certain datasets .
- The results indicated that focusing on lower-granularity labeling informed by clinical experts could lead to improved predictive performance .
Overall, the experiments conducted in the paper, along with the results obtained, provide strong empirical evidence to support the scientific hypotheses under investigation regarding the robustness and explainability of Language Models in identifying Wellness Dimensions related to mental health .
What are the contributions of this paper?
The paper "WellDunn: On the Robustness and Explainability of Language Models and Large Language Models in Identifying Wellness Dimensions" makes several key contributions:
- It introduces an evaluation design focusing on the robustness and explainability of Language Models (LMs) in identifying Wellness Dimensions (WD) .
- The paper highlights disparities between prediction accuracy and attention in LMs, emphasizing the need for a transparent classifier rooted in clinical understanding .
- It reveals surprising results about LMs and Large Language Models (LLMs), such as the alignment between attention and explanations remaining low, with LLMs scoring poorly in this aspect .
- The study emphasizes the importance of validating explanations against ground-truth physician practice to build trust in models working in real-world scenarios .
- Through benchmarking on domain-specific and general-purpose LMs, the paper underscores the necessity of a model that aligns explanation with clinical determination for mental health applications .
What work can be continued in depth?
Further research in the field of AI and mental health can be expanded in several areas based on the existing study:
- Robustness and Explainability: Future work can delve deeper into enhancing the robustness and explainability of AI-driven models in identifying mental health conditions. This includes focusing on ensuring that these models prioritize the correct clinically relevant terms for decision-making .
- Application to Various Mental Health Topics: The study suggests that the methodology used can be applied to a variety of other mental health topics beyond depression, anxiety, bipolar disorder, schizophrenia, and suicide risk. Exploring the effectiveness of language models in detecting causal cues across different conditions and addressing ethical considerations for conditions with higher stigma or vulnerability are areas that can be further investigated .
- Dataset Expansion and Model Evaluation: Researchers can explore applying the study results to other datasets, especially those incorporating multiple modalities of information beyond text. This can involve benchmarking different language models and evaluating their performance using robust evaluation metrics like the Matthews Correlation Coefficient (MCC) to ensure model trustworthiness and alignment with ground-truth clinical practices .