让作文评分可解释：按评分标准拆解写作要点
EssayCBM: Rubric-Aligned Concept Bottleneck Models for Transparent Essay Grading
- 将作文评分分解为8个可解释的写作概念，逐项评估
- 评分结果与主流模型持平，但过程透明可查
- 支持教师实时查看并修改评分依据，适合教育场景
自动作文评分（AES）虽借助神经语言模型取得显著进展，但多数系统仍缺乏透明度，难以揭示评分依据。在教育环境中，教师需要理解、信任并有时干预自动化评分决策。我们提出EssayCBM，一种与评分标准对齐的概念瓶颈框架，将作文评价分解为八个可解释的写作概念，再据此生成最终分数。不同于直接使用大模型评分的方法，EssayCBM学习从写作概念到分数的显式可审计映射，使教师能够检查并调整评分标准层面的预测。该方法在性能上媲美主流神经基线，同时实现评分过程的透明化与可编辑性。我们进一步构建了一个交互式系统，支持教师实时查看并修改各概念得分。
原文摘要 · Abstract (English)
Automated essay scoring (AES) has advanced significantly with neural language models, yet most systems remain opaque, offering little visibility into how grades are produced. In educational settings, instructors must be able to understand, trust, and occasionally override the automated grading decisions. We introduce EssayCBM, a rubric-aligned concept bottleneck framework that decomposes essay evaluation into eight interpretable writing concepts before computing the final score. Unlike direct LLM-based grading approaches, EssayCBM learns an explicit and auditable mapping from writing concepts to grades, allowing instructors to inspect and adjust rubric-level predictions during grading. EssayCBM matches neural AES baselines while making grading decisions transparent and directly editable at the rubric level. We further present an interactive system that demonstrates this capability by allowing instructors to inspect and modify concept predictions in real 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。