用大模型自动把邮件分发到对应学生群，省时省力
Content-Based Smart E-Mail Dispatcher Using Large Language Models
- 基于大模型的智能代理系统，根据邮件内容判断应转发至哪个学生群组
- 无需标注数据集，可实时分析邮件并准确路由信息，提升响应效率
- 适合高校教务、学生事务等需要批量处理邮件的场景
电子邮件已成为个人与职场生活的重要组成部分，但其海量信息处理仍对大型组织构成挑战。手动查阅邮件并将其内容和附件转发至其他即时通讯平台，不仅耗时且易出错，导致效率下降与额外压力。本文提出一种基于大语言模型（LLMs）的智能邮件分发机制，旨在自动化将邮件内容按内容精准推送至工程学院各年级学生对应的WhatsApp群组，实现组织内部信息的高效流转。系统通过结构化提示词框架，让代理调用大模型分析邮件文本，结合上下文指令判断目标群组，完成信息分发。该方法不依赖标注数据集，显著提升工作效率，降低阅读邮件带来的认知负担，适用于高校教务管理、学生通知等高频邮件处理场景。
原文摘要 · Abstract (English)
Email communication has become an integral part of personal and professional life, but handling its vast volume is still a significant issue for large organisations. Manual perusal of emails and forwarding their contents and attachments to intended recipients using other instant messaging platforms has proved to be error-prone and time-consuming leading to losses in terms of productivity and creating undue stress. The main objective of this paper is to explore an alternative mechanism that is to automate the task of dispatching emails based on their contents to the respective WhatsApp groups of students of various semesters of programs in an engineering college, facilitating a smooth flow of information from one end to another end in an organisation. The dispatcher system is built using agents querying large language models (LLMs) to enable it to analyze the contents of emails and route them to the relevant groups of students for their information and consumption. The system harnesses the capabilities of LLMs in analysing the textual contents for decision-making. With a well-structured agent framework prompt that includes email content as input with instructions and context, the system figures out the relevant groups to which the email message is dispatched, thus providing the required information on time. The proposed system does not rely on labelled datasets and provides several benefits, including enhanced productivity and a reduction in the cognitive load associated with reading emails.
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