大型语言模型用于协助美国大学申请
Large Language Models for Assisting American College Applications
AI总结：
EZCollegeApp 通过大型语言模型帮助高中生处理复杂大学申请流程，提供结构化表格、权威文件支持及人工控制，提升申请效率与准确性。
摘要：
美国大学申请要求学生 navigate 分散的录取政策、重复且有条件的表格以及模糊的问题，这些问题往往需要交叉参考多个来源。我们提出了 EZCollegeApp，一个基于大型语言模型（LLM）的系统，通过结构化申请表格、将建议答案基于权威录取文件进行支撑，并保持对最终回复的完整人工控制来协助高中生。该系统引入了先映射的范式，将表格理解与答案生成分开，使在异构申请门户之间能够保持一致的推理。EZCollegeApp 集成从官方录取网站获取文档、检索增强型问答以及一个带有建议和申请字段的人工在循环聊天机器人界面，而无需自动提交。我们描述了系统架构、数据管道、内部表示、安全和隐私措施以及通过自动化测试和人类质量评估进行的评估。我们的源代码已发布在 GitHub（https://github.com/ezcollegeapp-public/ezcollegeapp-public）以促进这项工作的更广泛影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
