LLM-KT：利用即插即用指令将大语言模型与知识追踪对齐
LLM-KT: Aligning Large Language Models with Knowledge Tracing using a Plug-and-Play Instruction
AI总结：
本文提出LLM-KT框架，通过即插即用指令、插件上下文和插件序列实现大语言模型与传统知识追踪序列模型的对齐，在四个数据集上取得最先进性能。
摘要：
知识追踪（KT）问题是个性化教育中极其重要的课题，旨在根据学生过去的答题记录预测其能否正确回答下一道问题。先前关于该任务的工作主要集中于基于ID或文本信息学习行为序列。然而，这些研究通常无法在结合关于问题的丰富世界知识进行推理的情况下，捕捉学生充分的行为模式。在本文中，我们提出了一个基于大语言模型（LLMs）的知识追踪框架，命名为LLM-KT，以整合LLMs与传统序列交互模型的优势。在任务级对齐方面，我们设计了即插即用指令（Plug-and-Play instruction）来使LLMs与知识追踪对齐，利用LLMs丰富的知识和强大的推理能力。在模态级对齐方面，我们设计了插件上下文（plug-in context）和插件序列（plug-in sequence）来整合传统方法学习到的多种模态。为了捕捉历史记录的长上下文，我们提出了一种插件上下文，利用问题特定和概念特定的token将压缩的上下文嵌入灵活地插入LLMs。此外，我们引入了一种插件序列，通过序列适配器（sequence adapter）将传统序列模型学习到的序列交互行为表示增强到LLMs中。大量实验表明，LLM-KT在四个典型数据集上与约20个强基线进行比较，取得了最先进的性能。
英文摘要：
The knowledge tracing (KT) problem is an extremely important topic in personalized education, which aims to predict whether students can correctly answer the next question based on their past question-answer records. Prior work on this task mainly focused on learning the sequence of behaviors based on the IDs or textual information. However, these studies usually fail to capture students' sufficient behavioral patterns without reasoning with rich world knowledge about questions. In this paper, we propose a large language models (LLMs)-based framework for KT, named \texttt{\textbf{LLM-KT}}, to integrate the strengths of LLMs and traditional sequence interaction models. For task-level alignment, we design Plug-and-Play instruction to align LLMs with KT, leveraging LLMs' rich knowledge and powerful reasoning capacity. For modality-level alignment, we design the plug-in context and sequence to integrate multiple modalities learned by traditional methods. To capture the long context of history records, we present a plug-in context to flexibly insert the compressed context embedding into LLMs using question-specific and concept-specific tokens. Furthermore, we introduce a plug-in sequence to enhance LLMs with sequence interaction behavior representation learned by traditional sequence models using a sequence adapter. Extensive experiments show that \texttt{\textbf{LLM-KT}} obtains state-of-the-art performance on four typical datasets by comparing it with approximately 20 strong baselines.
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。
