The field of AI-driven mental health and behavioral support is rapidly evolving, with a growing focus on developing personalized and empathetic care systems. Recent studies have explored the use of large language models (LLMs) in generating samples of user interactions for training reinforcement learning models, as well as in detecting mental health conditions and cyberbullying from social media data. Multimodal approaches, combining visual, audio, and textual features, have shown promise in improving the accuracy of harmful content detection and mental health support systems. The development of benchmarks and evaluation frameworks, such as MindEval and InvisibleBench, is also crucial for advancing the field and ensuring the safety and effectiveness of AI-driven systems. Noteworthy papers in this area include MindEval, which presents a framework for evaluating language models in multi-turn mental health therapy conversations, and InvisibleBench, which provides a deployment gate for caregiving-relationship AI. Overall, the field is moving towards more personalized, empathetic, and effective AI-driven mental health and behavioral support systems.
Advances in AI-Driven Mental Health and Behavioral Support
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Can we use LLMs to bootstrap reinforcement learning? -- A case study in digital health behavior change
MTikGuard System: A Transformer-Based Multimodal System for Child-Safe Content Moderation on TikTok
Optimal Meal Schedule for a Local Nonprofit Using LLM-Aided Data Extraction
MindEval: Benchmarking Language Models on Multi-turn Mental Health Support
Logic of Montage
Facilitating the Integration of LLMs Into Online Experiments With Simple Chat
A Nutrition Multimodal Photoplethysmography Language Model
Breaking Bad: Norms for Valence, Arousal, and Dominance for over 10k English Multiword Expressions
A Machine Learning Approach for Detection of Mental Health Conditions and Cyberbullying from Social Media
Adaptive LLM Agents: Toward Personalized Empathetic Care
When LLMs Can't Help: Real-World Evaluation of LLMs in Nutrition
MindSET: Advancing Mental Health Benchmarking through Large-Scale Social Media Data
InvisibleBench: A Deployment Gate for Caregiving Relationship AI
Training Introspective Behavior: Fine-Tuning Induces Reliable Internal State Detection in a 7B Model
The Need for Benchmarks to Advance AI-Enabled Player Risk Detection in Gambling