Large language models (LLMs) have been employed in various intelligent
educational tasks to assist teaching. While preliminary explorations have
focused on independent LLM-empowered agents for specific educational tasks, the
potential for LLMs within a multi-agent collaborative framework to simulate a
classroom with real user participation remains unexplored. In this work, we
propose SimClass, a multi-agent classroom simulation framework involving user
participation. We recognize representative class roles and introduce a novel
class control mechanism for automatic classroom teaching, and conduct user
experiments in two real-world courses. Utilizing the Flanders Interactive
Analysis System and Community of Inquiry theoretical frame works from
educational analysis, we demonstrate that LLMs can simulate traditional
classroom interaction patterns effectively while enhancing user's experience.
We also observe emergent group behaviors among agents in SimClass, where agents
collaborate to create enlivening interactions in classrooms to improve user
learning process. We hope this work pioneers the application of LLM-empowered
multi-agent systems in virtual classroom teaching.

提出了一个使用大型语言模型的多智能体课堂模拟框架 SimClass，在用户参与下实现传统教学模式的模拟，通过实验证明了多个代理之间的协作能够增强用户的学习体验，并希望这项工作能够推动大型语言模型在虚拟教学中的应用。

利用 LLM 强化代理模拟课堂教育

Simulating Classroom Education with LLM-Empowered Agents

Recent advancements in Text-to-SQL methods employing Large Language Models
(LLMs) have demonstrated remarkable performance. Nonetheless, these approaches
continue to encounter difficulties when handling extensive databases, intricate
user queries, and erroneous SQL results. To tackle these challenges, we present
\textbf{MAC-SQL}, a LLM-based multi-agent collaborative Text- to-SQL framework
based on LLMs. This framework comprises three agents: the \textit{Selector},
accountable for condensing voluminous databases and preserving relevant table
schemas for user questions; the \textit{Decomposer}, which disassembles complex
user questions into more straightforward sub-problems and resolves them
progressively; and the \textit{Refiner}, tasked with validating and refining
defective SQL queries. We perform thorough experiments on two Text-to-SQL
datasets, BIRD and Spider, attaining a state-of-the-art execution accuracy of
59.59\% on the BIRD test set. Moreover, we have open-sourced an instruction
fine-tuning model, \textbf{SQL-Llama}, based on Code Llama 7B, in addition to
an agent instruction dataset derived from training data based on BIRD and
Spider. The SQL-Llama model has demonstrated encouraging outcomes on the
development sets of both BIRD and Spider. However, when compared to the GPT-4
model, there remains a notable potential for enhancement. Our code and data can
be accessed publicly at
\href{https://github.com/wbbeyourself/MAC-SQL}{this https URL}.

最近的文本到 SQL 方法的发展使用了大型语言模型 (LLMs)，取得了显著的性能表现。然而，这些方法在处理庞大的数据库、复杂的用户查询和错误的 SQL 结果时仍然面临困难。为了解决这些挑战，我们提出了基于 LLMs 的多智能体协作文本到 SQL 框架 MAC-SQL。该框架由三个智能体组成：Selector 负责压缩庞大的数据库并保留用户问题的相关表模式；Decomposer 将复杂的用户问题分解为更简单的子问题并逐步解决；Refiner 负责验证和完善有缺陷的 SQL 查询。我们在两个 Text-to-SQL 数据集 BIRD 和 Spider 上进行了大量实验，在 BIRD 测试集上实现了 59.59% 的最先进的执行准确率。此外，我们还开源了基于 Code Llama 7B 的指导微调模型 SQL-Llama，以及基于 BIRD 和 Spider 训练数据的代理指令数据集。SQL-Llama 模型在 BIRD 和 Spider 的开发集上展示了令人鼓舞的结果，然而与 GPT-4 模型相比，仍有显著的提升潜力。我们的代码和数据可在 https://github.com/wbbeyourself/MAC-SQL 公开访问。