With the advent of off-the-shelf intelligent home products and broader internet adoption, researchers increasingly explore smart computing applications that provide easier access to health and wellness resources. AI-based systems like chatbots have the potential to provide services that could provide mental health support. However, existing therapy chatbots are often retrieval-based, requiring users to respond with a constrained set of answers, which may not be appropriate given that such pre-determined inquiries may not reflect each patient's unique circumstances. Generative-based approaches, such as the OpenAI GPT models, could allow for more dynamic conversations in therapy chatbot contexts than previous approaches. To investigate the generative-based model's potential in therapy chatbot contexts, we built a chatbot using the GPT-2 model. We fine-tuned it with 306 therapy session transcripts between family caregivers of individuals with dementia and therapists conducting Problem Solving Therapy. We then evaluated the model's pre-trained and the fine-tuned model in terms of basic qualities using three meta-information measurements: the proportion of non-word outputs, the length of response, and sentiment components. Results showed that: (1) the fine-tuned model created more non-word outputs than the pre-trained model; (2) the fine-tuned model generated outputs whose length was more similar to that of the therapists compared to the pre-trained model; (3) both the pre-trained model and fine-tuned model were likely to generate more negative and fewer positive outputs than the therapists. We discuss potential reasons for the problem, the implications, and solutions for developing therapy chatbots and call for investigations of the AI-based system application.
翻译:随着现成的智能家庭产品和更广泛的互联网应用的出现,研究人员越来越多地探索智能计算应用软件,从而更容易获得健康和健康资源。基于AI的系统,如聊天机,具有提供心理健康支持的潜力。然而,现有的治疗性聊天机往往以检索为基础,要求用户用一套有限的答案作出回应,这也许不合适,因为这种预先确定的查询可能不反映每个病人的独特情况。基于创举的方法,如OpenAI GPT模型,可以比以前的方法,在治疗性聊天机环境中进行更活跃的对话。为了调查基于基因的模型在治疗性聊天机环境中的潜力,我们用GPT-2模型建立了一种聊天机。我们用306次治疗性会议记录对患有痴呆症的个人的家庭护理者与进行治疗性病前治疗治疗者之间的治疗记录进行了微调,我们随后用三种基于正面信息的测量方法,即非字型产出的比例、精细反应的长度和感官调查,我们用这种分析的结果显示:(1)对模型进行微调的模型的模型和微调的模型的模型的应用,比模型和感官分析的输出更精确地解释了。