2 citations · 4 across the 8 of their papers we have counts for
8 papers
Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges
Yisong Chen, Yifan Gao, Sijing Yu +2
We present a review on the applications of large language models (LLMs) in health, e.g., social media analysis, clinical conversational agents, therapy support tools, prompt engine…
Transparent Reference-free Automated Evaluation of Open-Ended User Survey Responses
Subin An, Yugyeong Ji, Junyoung Kim +3
Open-ended survey responses provide valuable insights in marketing research, but low-quality responses not only burden researchers with manual filtering but also risk leading to mi…
Toward Corpus Size Requirements for Training and Evaluating Depression Risk Models Using Spoken Language
Tomek Rutowski, Amir Harati, Elizabeth Shriberg +3
Mental health risk prediction is a growing field in the speech community, but many studies are based on small corpora. This study illustrates how variations in test and train set s…
Optimizing Speech-Input Length for Speaker-Independent Depression Classification
Tomasz Rutowski, Amir Harati, Yang Lu +1
Machine learning models for speech-based depression classification offer promise for health care applications. Despite growing work on depression classification, little is understo…
Depression and Anxiety Prediction Using Deep Language Models and Transfer Learning
Tomasz Rutowski, Elizabeth Shriberg, Amir Harati +3
Digital screening and monitoring applications can aid providers in the management of behavioral health conditions. We explore deep language models for detecting depression, anxiety…
Cross-Demographic Portability of Deep NLP-Based Depression Models
Tomek Rutowski, Elizabeth Shriberg, Amir Harati +3
Deep learning models are rapidly gaining interest for real-world applications in behavioral health. An important gap in current literature is how well such models generalize over d…
Speech-Based Depression Prediction Using Encoder-Weight-Only Transfer Learning and a Large Corpus
Amir Harati, Elizabeth Shriberg, Tomasz Rutowski +3
Speech-based algorithms have gained interest for the management of behavioral health conditions such as depression. We explore a speech-based transfer learning approach that uses a…
Illuminating the Black Box: A Psychometric Investigation into the Multifaceted Nature of Large Language Models
Yang Lu, Jordan Yu, Shou-Hsuan Stephen Huang
This study explores the idea of AI Personality or AInality suggesting that Large Language Models (LLMs) exhibit patterns similar to human personalities. Assuming that LLMs share th…