CASTLE：一个评估大语言模型学生定制个性化安全性的综合基准
CASTLE: A Comprehensive Benchmark for Evaluating Student-Tailored Personalized Safety in Large Language Models
AI总结：
CASTLE基准通过评估学生定制个性化安全性，揭示大语言模型在不同学生属性下的安全缺陷。
摘要：
大语言模型（LLMs）在教育个性化学习的发展中取得了进展。然而，其固有的生成机制往往对相同的提示产生同质化的响应。这种一刀切的机制忽视了学生认知和心理的显著异质性，从而对易受影响群体构成潜在的安全风险。现有的安全评估主要依赖于上下文无关的指标，如事实准确性、偏见或毒性，这些指标无法捕捉到相同响应在不同学生属性下可能造成的不同危害。为了解决这一差距，我们提出了学生定制个性化安全性的概念，并基于教育理论构建了CASTLE基准。该基准涵盖15种教育安全风险和14种学生属性，包含92,908个双语场景。我们进一步设计了三个评估指标：风险敏感度，衡量模型检测风险的能力；情感共情，评估模型识别学生状态的能力；以及学生匹配度，评估模型响应与学生属性的匹配程度。在18个最先进的LLM上进行的实验表明，CASTLE构成了重大挑战：所有模型的平均安全评分低于5分的2.3分，表明在个性化安全保证方面存在显著缺陷。
英文摘要：
Large language models (LLMs) have advanced the development of personalized learning in education. However, their inherent generation mechanisms often produce homogeneous responses to identical prompts. This one-size-fits-all mechanism overlooks the substantial heterogeneity in students cognitive and psychological, thereby posing potential safety risks to vulnerable groups. Existing safety evaluations primarily rely on context-independent metrics such as factual accuracy, bias, or toxicity, which fail to capture the divergent harms that the same response might cause across different student attributes. To address this gap, we propose the concept of Student-Tailored Personalized Safety and construct CASTLE based on educational theories. This benchmark covers 15 educational safety risks and 14 student attributes, comprising 92,908 bilingual scenarios. We further design three evaluation metrics: Risk Sensitivity, measuring the model ability to detect risks; Emotional Empathy, evaluating the model capacity to recognize student states; and Student Alignment, assessing the match between model responses and student attributes. Experiments on 18 SOTA LLMs demonstrate that CASTLE poses a significant challenge: all models scored below an average safety rating of 2.3 out of 5, indicating substantial deficiencies in personalized safety assurance.
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。
