用表格基础模型实现实时学习追踪，速度提升53倍
Live Knowledge Tracing: Real-Time Adaptation using Tabular Foundation Models
- 通过上下文学习在推理时匹配训练数据，跳过传统训练
- 在多数据集上表现媲美传统模型，平均提速53倍
- 适合需要快速响应的在线教育系统
深度知识追踪模型在建模学生学习轨迹方面取得了显著进展。然而，这些架构需要大量训练时间，且在序列较短的数据集上容易过拟合。本文探索了一种新的知识追踪范式，利用表格基础模型（TFMs）。与传统方法需在固定训练集上离线训练不同，我们的方法通过上下文学习实现实时“在线”知识追踪，无需训练步骤。TFMs 在推理时将测试序列与相关训练序列对齐，从而跳过训练阶段。我们使用多个规模递增的数据集验证，该方法在学生交互逐步观测的场景下，预测性能具有竞争力，平均提速达53倍。
原文摘要 · Abstract (English)
Deep knowledge tracing models have achieved significant breakthroughs in modeling student learning trajectories. However, these architectures require substantial training time and are prone to overfitting on datasets with short sequences. In this paper, we explore a new paradigm for knowledge tracing by leveraging tabular foundation models (TFMs). Unlike traditional methods that require offline training on a fixed training set, our approach performs real-time ''live'' knowledge tracing in an online way via in-context learning. TFMs align testing sequences with relevant training sequences at inference time, therefore skipping the training step entirely. We demonstrate, using several datasets of increasing size, that our method achieves competitive predictive performance with up to 53x speedups on average, in a setting where student interactions are observed progressively over time.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。