This study explores the integration of the ChatGPT API with GPT-4 model and
Microsoft Copilot Studio on the Microsoft Teams platform to develop an
intelligent tutoring system. Designed to provide instant support to students,
the system dynamically adjusts educational content in response to the learners'
progress and feedback. Utilizing advancements in natural language processing
and machine learning, it interprets student inquiries, offers tailored
feedback, and facilitates the educational journey. Initial implementation
highlights the system's potential in boosting students' motivation and
engagement, while equipping educators with critical insights into the learning
process, thus promoting tailored educational experiences and enhancing
instructional effectiveness.

利用自然语言处理和机器学习的进展，该研究探讨了在 Microsoft Teams 平台上将 ChatGPT API 与 GPT-4 模型和 Microsoft Copilot Studio 结合，开发智能辅导系统的可能性。该系统旨在为学生提供即时支持，根据学习者的进展和反馈动态调整教育内容，并解释学生的问题、提供个性化的反馈，并促进学习过程。初步实施突显了该系统在提升学生的动力和参与度方面的潜力，同时为教育者提供了对学习过程的关键洞察力，从而推广了个性化的教育体验并增强了教学效果。

智能导师：在 Teams 中利用 ChatGPT 和 Microsoft Copilot Studio 提供生成式人工智能学生支持和反馈系统

Intelligent Tutor: Leveraging ChatGPT and Microsoft Copilot Studio to  Deliver a Generative AI Student Support and Feedback System within Teams

The widespread adoption of social media platforms globally not only enhances
users' connectivity and communication but also emerges as a vital channel for
the dissemination of health-related information, thereby establishing social
media data as an invaluable organic data resource for public health research.
The surge in popularity of vaping or e-cigarette use in the United States and
other countries has caused an outbreak of e-cigarette and vaping use-associated
lung injury (EVALI), leading to hospitalizations and fatalities in 2019,
highlighting the urgency to comprehend vaping behaviors and develop effective
strategies for cession. In this study, we extracted a sample dataset from one
vaping sub-community on Reddit to analyze users' quit vaping intentions.
Leveraging large language models including both the latest GPT-4 and
traditional BERT-based language models for sentence-level quit-vaping intention
prediction tasks, this study compares the outcomes of these models against
human annotations. Notably, when compared to human evaluators, GPT-4 model
demonstrates superior consistency in adhering to annotation guidelines and
processes, showcasing advanced capabilities to detect nuanced user quit-vaping
intentions that human evaluators might overlook. These preliminary findings
emphasize the potential of GPT-4 in enhancing the accuracy and reliability of
social media data analysis, especially in identifying subtle users' intentions
that may elude human detection.

社交媒体数据分析方面的研究，着重于在 Reddit 社交平台中提取烟民戒烟意图的数据，并通过比较 GPT-4 模型与人类评估结果来展示其在检测微妙用户戒烟意图方面的优势。

利用大型语言模型识别考虑数字干预戒烟的 Reddit 用户

Utilizing Large Language Models to Identify Reddit Users Considering  Vaping Cessation for Digital Interventions

Formal verification can provably guarantee the correctness of critical system
software, but the high proof burden has long hindered its wide adoption.
Recently, Large Language Models (LLMs) have shown success in code analysis and
synthesis. In this paper, we present a combination of LLMs and static analysis
to synthesize invariants, assertions, and other proof structures for a
Rust-based formal verification framework called Verus. In a few-shot setting,
LLMs demonstrate impressive logical ability in generating postconditions and
loop invariants, especially when analyzing short code snippets. However, LLMs
lack the ability to retain and propagate context information, a strength of
traditional static analysis. Based on these observations, we developed a
prototype based on OpenAI's GPT-4 model. Our prototype decomposes the
verification task into multiple smaller ones, iteratively queries GPT-4, and
combines its output with lightweight static analysis. We evaluated the
prototype with a developer in the automation loop on 20 vector-manipulating
programs. The results demonstrate that it significantly reduces human effort in
writing entry-level proof code.

基于大型语言模型（LLMs）和静态分析相结合，开发了一个基于 Rust 的形式验证框架 Verus 的原型。通过将验证任务分解为多个较小的任务，迭代地查询 GPT-4，并将其输出与轻量级静态分析相结合，这个原型显著减少了编写入门级证明代码的人力工作。