用大模型帮高中生理清美国大学申请的复杂流程
Large Language Models for Assisting American College Applications
- 先解析表单再生成答案，跨平台逻辑一致
- 答案基于官方招生文件，准确率高且可追溯
- 全程人工审核，适合希望自主掌控申请的学生
美国大学申请流程复杂，学生需应对分散的招生政策、重复且条件复杂的表格以及需要多方查证的模糊问题。本文提出EZCollegeApp，一个基于大语言模型（LLM）的系统，帮助高中生结构化处理申请表单，将建议答案锚定在权威招生文件上，并保持最终回答的完全人工控制。该系统采用“映射优先”范式，将表单理解与答案生成分离，实现异构申请门户间的稳定推理。系统整合了来自官方招生网站的文档摄入、检索增强问答，以及人机协同的聊天界面，仅提供建议而不自动提交。我们介绍了系统架构、数据流程、内部表示、安全与隐私措施，并通过自动化测试和人工质量评估进行验证。源代码已开源，以促进更广泛影响。
原文摘要 · Abstract (English)
American college applications require students to navigate fragmented admissions policies, repetitive and conditional forms, and ambiguous questions that often demand cross-referencing multiple sources. We present EZCollegeApp, a large language model (LLM)-powered system that assists high-school students by structuring application forms, grounding suggested answers in authoritative admissions documents, and maintaining full human control over final responses. The system introduces a mapping-first paradigm that separates form understanding from answer generation, enabling consistent reasoning across heterogeneous application portals. EZCollegeApp integrates document ingestion from official admissions websites, retrieval-augmented question answering, and a human-in-the-loop chatbot interface that presents suggestions alongside application fields without automated submission. We describe the system architecture, data pipeline, internal representations, security and privacy measures, and evaluation through automated testing and human quality assessment. Our source code is released on GitHub (https://github.com/ezcollegeapp-public/ezcollegeapp-public) to facilitate the broader impact of this work.
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