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Hugging Face Daily PapersHe Hu, Yucheng Zhou, Qianning Wang2 min readpaperintermediate

From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health

Read on arxiv.org

Summary

The paper surveys the rapidly growing literature on applying large language models to mental health, organizing it into three evolutionary phases from passive information tools to stateful personalized companions. It reviews core technologies, agent architectures, datasets, and benchmarks, and outlines challenges and a roadmap for responsible, effective AI‑driven mental health support.

  • LLM applications in mental health are framed in three phases: information tools, empathetic conversationalists, and longitudinal personalized companions.
  • Phase III agents need profile, memory, reasoning, and planning components to maintain state across sessions.
  • The survey catalogs over 40 datasets/benchmarks and highlights the lack of longitudinal evaluation and privacy‑preserving resources.
  • It emphasizes ethical risks—misinformation, bias, privacy—and calls for responsible design guidelines.

Engineers building or evaluating LLM‑based health applications need this structured overview to understand current capabilities, data needs, and ethical pitfalls.

6/10
llmaihealthcareethicsmental health

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