用大模型提升记忆复习效率，专攻语言考试中的易混淆词汇。
LECTOR: LLM-Enhanced Concept-based Test-Oriented Repetition for Adaptive Spaced Learning
- 结合大模型语义分析与间隔重复，动态优化复习时间点。
- 测试中达成90.2%正确率，比最优基线提升2.0%。
- 适合备考语言考试者，尤其对抗相似词混淆有优势。
间隔重复系统是高效学习与记忆保持的基础，但现有算法在语义干扰和个性化适应方面表现不佳。我们提出 LECTOR（LLM-Enhanced Concept-based Test-Oriented Repetition），一种专为以考试为导向的学习场景设计的自适应调度算法，尤其适用于语言考试中对通过率要求高的情况。LECTOR 利用大语言模型进行语义分析，并融合个性化学习档案，通过大模型驱动的语义相似性评估解决词汇学习中的语义混淆问题，同时结合经典的间隔重复原则。我们在100名模拟学习者上，持续100天，对比六种基线算法（SSP-MMC、SM2、HLR、FSRS、ANKI、THRESHOLD）进行综合评估，结果表明：LECTOR 达到90.2%的通过率，优于最佳基线（SSP-MMC）的88.4%，相对提升2.0%。该算法在处理语义相近概念时表现尤为突出，显著降低因混淆导致的错误，同时保持计算效率。研究结果证明 LECTOR 是智能辅导系统与自适应学习平台的有力候选方案。
原文摘要 · Abstract (English)
Spaced repetition systems are fundamental to efficient learning and memory retention, but existing algorithms often struggle with semantic interference and personalized adaptation. We present LECTOR (\textbf{L}LM-\textbf{E}nhanced \textbf{C}oncept-based \textbf{T}est-\textbf{O}riented \textbf{R}epetition), a novel adaptive scheduling algorithm specifically designed for test-oriented learning scenarios, particularly language examinations where success rate is paramount. LECTOR leverages large language models for semantic analysis while incorporating personalized learning profiles, addressing the critical challenge of semantic confusion in vocabulary learning by utilizing LLM-powered semantic similarity assessment and integrating it with established spaced repetition principles. Our comprehensive evaluation against six baseline algorithms (SSP-MMC, SM2, HLR, FSRS, ANKI, THRESHOLD) across 100 simulated learners over 100 days demonstrates significant improvements: LECTOR achieves a 90.2\% success rate compared to 88.4\% for the best baseline (SSP-MMC), representing a 2.0\% relative improvement. The algorithm shows particular strength in handling semantically similar concepts, reducing confusion-induced errors while maintaining computational efficiency. Our results establish LECTOR as a promising direction for intelligent tutoring systems and adaptive learning platforms.
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