This article presents a method for prompt-based mental health screening from
a large and noisy dataset of social media text. Our method uses GPT 3.5.
prompting to distinguish publications that may be more relevant to the task,
and then uses a straightforward bag-of-words text classifier to predict actual
user labels. Results are found to be on pair with a BERT mixture of experts
classifier, and incurring only a fraction of its computational costs.

本文介绍了一种从大规模、嘈杂的社交媒体文本数据集中进行基于提示的心理健康筛查的方法。我们的方法使用 GPT 3.5 进行提示，以区分可能与任务更相关的出版物，然后使用直观的词袋文本分类器来预测实际用户标签。结果发现与 BERT 专家混合分类器相当，并且只需要一小部分计算成本。

基于提示的社交媒体文本精简中文专业翻译

Prompt-based mental health screening from social media text

We present a method for automatically constructing a concept hierarchy for a
given domain by querying a large language model. We apply this method to
various domains using OpenAI's GPT 3.5. Our experiments indicate that LLMs can
be of considerable help for constructing concept hierarchies.

通过查询大型语言模型，我们提出了一种用于自动构建给定领域概念层级的方法，我们使用 OpenAI 的 GPT 3.5 将该方法应用于不同领域，实验证明 LLM 在构建概念层级方面具有相当的帮助。

面向语言模型的本体构建

Towards Ontology Construction with Language Models

The debate around the use of GPT 3.5 has been a popular topic among academics
since the release of ChatGPT. Whilst some have argued for the advantages of GPT
3.5 in enhancing academic writing, others have raised concerns such as
plagiarism, the spread of false information, and ecological issues. The need
for finding ways to use GPT 3.5 models transparently has been voiced, and
suggestions have been made on social media as to how to use GPT 3.5 models in a
smart way. Nevertheless, to date, there is a lack of literature which clearly
outlines how to use GPT 3.5 models in academic writing, how effective they are,
and how to use them transparently. To address this, I conducted a personal
experience experiment with GPT 3.5, specifically by using OpenAI text davinci
003 model, for writing this article. I identified five ways of using GPT 3.5:
Chunk Stylist, Bullet to Paragraph, Talk Textualizer, Research Buddy, and
Polisher. I reflected on their efficacy, and commented on their potential
impact on writing ethics. Additionally, I provided a comprehensive document
which shows the prompts I used, results I got from GPT 3.5, the final edits and
visually compares those by showing the differences in percentages.

本文通过自己进行的实验，使用 OpenAI text davinci 003 模型，找到了五种使用 GPT 3.5 模型的方式，并反思了它们的有效性以及对写作伦理的潜在影响。同时提供了详细的实验记录并展示了修改前后的差异。