用微调+检索增强生成，让大学招生回复又快又准
Enhancing Admission Inquiry Responses with Fine-Tuned Models and Retrieval-Augmented Generation
- 用招生数据微调语言模型，提升对复杂规则的理解力
- 结合检索与微调，使回复准确率显著高于纯检索方案
- 适合需要高精度、快响应的高校招生智能客服场景
大学招生办公室面临海量咨询压力，如何在保证回复质量的同时提升响应速度，直接影响潜在学生的感受。本文提出一种融合微调语言模型与检索增强生成（RAG）的AI系统。尽管RAG能从大规模数据中检索信息，但在大学招生这类细节繁复、规则严谨的垂直领域表现受限，易产生不贴切的回复。为此，我们基于精心整理的招生流程数据集对模型进行微调，使其更精准理解RAG提供的信息，并生成符合领域特性的回答。该混合方法兼具RAG的实时信息获取能力与微调模型的领域知识深度。此外，我们优化了生成逻辑，在响应质量和速度间取得平衡，确保招生沟通始终高效且高质量。
原文摘要 · Abstract (English)
University admissions offices face the significant challenge of managing high volumes of inquiries efficiently while maintaining response quality, which critically impacts prospective students' perceptions. This paper addresses the issues of response time and information accuracy by proposing an AI system integrating a fine-tuned language model with Retrieval-Augmented Generation (RAG). While RAG effectively retrieves relevant information from large datasets, its performance in narrow, complex domains like university admissions can be limited without adaptation, potentially leading to contextually inadequate responses due to the intricate rules and specific details involved. To overcome this, we fine-tuned the model on a curated dataset specific to admissions processes, enhancing its ability to interpret RAG-provided data accurately and generate domain-relevant outputs. This hybrid approach leverages RAG's ability to access up-to-date information and fine-tuning's capacity to embed nuanced domain understanding. We further explored optimization strategies for the response generation logic, experimenting with settings to balance response quality and speed, aiming for consistently high-quality outputs that meet the specific requirements of admissions communications.
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