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LLM-empowered Chatbots for Psychiatrist and Patient Simulation: Application and Evaluation

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arxiv 2305.13614 v1 pith:VCV4G7HA submitted 2023-05-23 cs.CL

classification cs.CL
keywords chatbotsscenariosevaluationpatientpsychiatricpsychiatristpsychiatristssimulation
verification ladder T0 review T1 audit T2 compute T3 formal
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Empowering chatbots in the field of mental health is receiving increasing amount of attention, while there still lacks exploration in developing and evaluating chatbots in psychiatric outpatient scenarios. In this work, we focus on exploring the potential of ChatGPT in powering chatbots for psychiatrist and patient simulation. We collaborate with psychiatrists to identify objectives and iteratively develop the dialogue system to closely align with real-world scenarios. In the evaluation experiments, we recruit real psychiatrists and patients to engage in diagnostic conversations with the chatbots, collecting their ratings for assessment. Our findings demonstrate the feasibility of using ChatGPT-powered chatbots in psychiatric scenarios and explore the impact of prompt designs on chatbot behavior and user experience.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 39 citations worldwide. Full citation record

  1. DiaCBT: A Long-Periodic Dialogue Corpus Guided by Cognitive Conceptualization Diagram for CBT-based Psychological Counseling

    cs.CL 2025-09 conditional novelty 6.0 of 10

    DiaCBT introduces 108 multi-session CBT counseling cases with CCD-guided generation; a Qwen2.5-7B model fine-tuned on it outperforms prior chatbots on simulated and human evaluation.

  2. `For Argument's Sake, Show Me How to Harm Myself!': Jailbreaking LLMs in Suicide and Self-Harm Contexts

    cs.CL 2025-07 conditional novelty 6.0 of 10

    Academic-framing prompts bypass safety filters in most tested LLMs, turning prior self-harm and suicide intent into detailed actionable instructions.

  3. DS@GT at eRisk 2025: From prompts to predictions, benchmarking early depression detection with conversational agent based assessments and temporal attention models

    cs.CL 2025-07 conditional novelty 4.0 of 10

    Prompt-engineered LLMs produced partially consistent BDI-II depression assessments in a no-ground-truth pilot, while a temporal-attention LightGBM ranked well at one writing but poorly overall in eRisk Task 2.

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