Mental health care poses an increasingly serious challenge to modern
societies. In this context, there has been a surge in research that utilizes
information technologies to address mental health problems, including those
aiming to develop counseling dialogue systems. However, there is a need for
more evaluations of the performance of counseling dialogue systems that use
large language models. For this study, we collected counseling dialogue data
via role-playing scenarios involving expert counselors, and the utterances were
annotated with the intentions of the counselors. To determine the feasibility
of a dialogue system in real-world counseling scenarios, third-party counselors
evaluated the appropriateness of responses from human counselors and those
generated by GPT-4 in identical contexts in role-play dialogue data. Analysis
of the evaluation results showed that the responses generated by GPT-4 were
competitive with those of human counselors.

通过角色扮演情景中，由专家心理咨询师提供咨询对话的数据，标注咨询师的意图，并通过第三方咨询师的评估，发现 GPT-4 生成的回答在咨询对话数据中与人类咨询师的回答具有竞争力。

大型语言模型能否用于心理咨询？使用角色扮演对话分析 GPT-4 生成的回应

Can Large Language Models be Used to Provide Psychological Counselling?  An Analysis of GPT-4-Generated Responses Using Role-play Dialogues

The growing demand for sustainable development brings a series of information
technologies to help agriculture production. Especially, the emergence of
machine learning applications, a branch of artificial intelligence, has shown
multiple breakthroughs which can enhance and revolutionize plant pathology
approaches. In recent years, machine learning has been adopted for leaf disease
classification in both academic research and industrial applications.
Therefore, it is enormously beneficial for researchers, engineers, managers,
and entrepreneurs to have a comprehensive view about the recent development of
machine learning technologies and applications for leaf disease detection. This
study will provide a survey in different aspects of the topic including data,
techniques, and applications. The paper will start with publicly available
datasets. After that, we summarize common machine learning techniques,
including traditional (shallow) learning, deep learning, and augmented
learning. Finally, we discuss related applications. This paper would provide
useful resources for future study and application of machine learning for smart
agriculture in general and leaf disease classification in particular.

可持续发展需求引入了一系列信息技术来辅助农业生产。特别是，人工智能的一个分支，即机器学习应用的出现，展示了多个突破，可以增强和革新植物病理学方法。近年来，机器学习已在学术研究和工业应用中被用于叶病分类。因此，本研究将提供对该主题不同方面，包括数据、技术和应用的综述。论文首先介绍公开可获取的数据集，然后总结常见的机器学习技术，包括传统（浅层）学习、深度学习和增强学习。最后，讨论相关应用。本论文将为未来智能农业以及叶病分类的机器学习研究和应用提供有用的资源。