针对课程大纲设计了双损失微调嵌入模型，提升教育语义检索准确率。
An Open-Source Dual-Loss Embedding Model for Semantic Retrieval in Higher Education
- 用合成数据+LLM生成构建3197对语义匹配句对，适配学术语言特点。
- 双损失模型在28个大学课程大纲上超越主流开源模型，接近闭源领先水平。
- 开源可复现，适合开发智能助教、知识库问答等教育应用。
近年来人工智能的发展推动了智能教育工具的普及，但现有语义检索系统仍难以适应学术内容独特的语言与结构特征。本研究提出两个针对教育问答任务微调的开源嵌入模型，特别适用于课程大纲场景。通过人工筛选与大语言模型辅助生成，构建了一个包含3,197对句子的合成数据集，涵盖同义术语、改写问题及隐含显性映射关系。评估了两种训练策略：(1) 使用MultipleNegativesRankingLoss（MNRL）的基线模型；(2) 结合MNRL与CosineSimilarityLoss的双损失模型，以优化语义排序与相似度校准。在28所大学课程大纲上，使用固定自然语言问题集（分属课程、教师、助教信息类别）进行测试。结果表明，两个微调模型均优于强基准模型（如all-MiniLM-L6-v2和multi-qa-MiniLM-L6-cos-v1），且双损失模型显著缩小了与OpenAI text-embedding-3系列等高性能闭源模型的差距。本研究贡献了可复用、领域对齐的嵌入模型，并提供可复制的教育语义检索框架，支持智能助教、检索增强生成（RAG）系统及学习管理系统（LMS）集成等下游应用。
原文摘要 · Abstract (English)
Recent advances in AI have catalyzed the adoption of intelligent educational tools, yet many semantic retrieval systems remain ill-suited to the unique linguistic and structural characteristics of academic content. This study presents two open-source embedding models fine-tuned for educational question answering, particularly in the context of course syllabi. A synthetic dataset of 3,197 sentence pairs, spanning synonymous terminology, paraphrased questions, and implicit-explicit mappings, was constructed through a combination of manual curation and large language model (LLM)-assisted generation. Two training strategies were evaluated: (1) a baseline model fine-tuned using MultipleNegativesRankingLoss (MNRL), and (2) a dual-loss model that combines MNRL with CosineSimilarityLoss to improve both semantic ranking and similarity calibration. Evaluations were conducted on 28 university course syllabi using a fixed set of natural language questions categorized into course, faculty, and teaching assistant information. Results demonstrate that both fine-tuned models outperform strong open-source baselines, including all-MiniLM-L6-v2 and multi-qa-MiniLM-L6-cos-v1, and that the dual-loss model narrows the performance gap with high-performing proprietary embeddings such as OpenAI's text-embedding-3 series. This work contributes reusable, domain-aligned embedding models and provides a replicable framework for educational semantic retrieval, supporting downstream applications such as academic chatbots, retrieval-augmented generation (RAG) systems, and learning management system (LMS) integrations.
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