用语言模型生成可解释的学生知识状态描述，兼顾准确与可读性。
Language Bottleneck Models for Qualitative Knowledge State Modeling
- 编码器大模型生成文本化知识状态，解码器预测未来表现
- 在真实和合成数据上均实现高精度，且样本效率优于传统方法
- 能捕捉误解等定性信息，适合教育评估与教学反馈场景
准确评估学生知识水平是教育的核心。认知诊断（CD）模型在特定时间点估计学生能力，而知识追踪（KT）方法则建模知识状态演变以预测未来表现。然而，现有方法或仅提供量化掌握程度但表达力有限（如CD、概率型KT），或为追求预测准确性牺牲可解释性（深度学习型KT）。本文提出语言瓶颈模型（LBMs），其中编码器大模型生成文本形式的知识状态摘要，解码器大模型据此预测未来表现。该方法生成的摘要具备可解释性，能够表达误解等细微洞察，是传统CD与KT模型无法捕捉的。在合成与真实世界数据集上的广泛验证表明，LBMs不仅揭示了超越传统模型的定性见解，还实现了具有竞争力的预测准确率，并展现出更优的样本效率。我们进一步证明，通过强化学习微调编码器、监督微调解码器，可同步提升摘要质量与预测性能。
原文摘要 · Abstract (English)
Accurately assessing student knowledge is central to education. Cognitive Diagnosis (CD) models estimate student proficiency at a fixed point in time, while Knowledge Tracing (KT) methods model evolving knowledge states to predict future performance. However, existing approaches either provide quantitative concept mastery estimates with limited expressivity (CD, probabilistic KT) or prioritize predictive accuracy at the cost of interpretability (deep learning KT). We propose Language Bottleneck Models (LBMs), where an encoder LLM produces textual knowledge state summaries, which a decoder LLM uses to predict future performance. This produces interpretable summaries that can express nuanced insights--such as misconceptions--that CD and KT models cannot capture. Extensive validation across synthetic and real-world datasets shows LBMs reveal qualitative insights beyond what CD and KT models can capture, while achieving competitive accuracy with improved sample efficiency. We demonstrate that the encoder and decoder can be fine-tuned with reinforcement learning and supervised fine-tuning respectively to improve both summary quality and predictive performance.
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