EMNLP: 教育者角色道德与规范大型语言模型画像
EMNLP: Educator-role Moral and Normative Large Language Models Profiling
AI总结：
本文提出EMNLP框架，用于评估教育者角色LLM的伦理和心理一致性，通过构建88个教师特定道德困境，揭示教师角色LLM在道德推理和情感复杂情境中的表现差异。
摘要：
模拟职业（SP）使大型语言模型（LLMs）能够模拟专业角色。然而，在这些背景下进行全面的心理和伦理评估仍然不足。本文介绍了EMNLP，一种用于人格画像、道德发展阶段测量和伦理风险评估的教育者角色道德与规范LLM画像框架，通过软提示注入进行评估。EMNLP扩展了现有量表，构建了88个教师特定的道德困境，使专业导向的比较成为可能。一个针对教师SP的定向软提示注入集评估了教师在合规性和脆弱性方面的表现。在14个LLM上的实验显示，教师角色LLM表现出比人类教师更加理想化和极化的性格，擅长抽象道德推理，但在情感复杂情境中表现不佳。推理能力更强的模型对有害提示注入更脆弱，揭示了能力与安全之间的悖论。模型温度和其他超参数在某些风险行为中影响有限。本文提出了首个评估教育AI中教师角色LLM伦理和心理一致性的基准。资源可在https://e-m-n-l-p.github.io/上获取。
英文摘要：
Simulating Professions (SP) enables Large Language Models (LLMs) to emulate professional roles. However, comprehensive psychological and ethical evaluation in these contexts remains lacking. This paper introduces EMNLP, an Educator-role Moral and Normative LLMs Profiling framework for personality profiling, moral development stage measurement, and ethical risk under soft prompt injection. EMNLP extends existing scales and constructs 88 teacher-specific moral dilemmas, enabling profession-oriented comparison with human teachers. A targeted soft prompt injection set evaluates compliance and vulnerability in teacher SP. Experiments on 14 LLMs show teacher-role LLMs exhibit more idealized and polarized personalities than human teachers, excel in abstract moral reasoning, but struggle with emotionally complex situations. Models with stronger reasoning are more vulnerable to harmful prompt injection, revealing a paradox between capability and safety. The model temperature and other hyperparameters have limited influence except in some risk behaviors. This paper presents the first benchmark to assess ethical and psychological alignment of teacher-role LLMs for educational AI. Resources are available at https://e-m-n-l-p.github.io/.
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。
