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This paper presents a novel framework for quantitatively evaluating theinteractive ChatGPT model in the context of suicidality assessment from socialmedia posts, utilizing the University of Maryland Reddit suicidality dataset.We conduct a technical evaluation of ChatGPT's performance on this task usingZero-Shot and Few-Shot experiments and compare its results with those of twofine-tuned transformer-based models. Additionally, we investigate the impact ofdifferent temperature parameters on ChatGPT's response generation and discussthe optimal temperature based on the inconclusiveness rate of ChatGPT. Ourresults indicate that while ChatGPT attains considerable accuracy in this task,transformer-based models fine-tuned on human-annotated datasets exhibitsuperior performance. Moreover, our analysis sheds light on how adjusting theChatGPT's hyperparameters can improve its ability to assist mental healthprofessionals in this critical task.

ChatGPT for Suicide Risk Assessment on Social Media: Quantitative Evaluation of Model Performance, Potentials and Limitations