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Assessing instruction quality is a fundamental component of any improvement efforts in the education system. However, traditional manual assessments are expensive, subjective, and heavily dependent on observers' expertise and idiosyncratic…

计算与语言 · 计算机科学 2025-01-03 Paiheng Xu , Jing Liu , Nathan Jones , Julie Cohen , Wei Ai

Large Language Models (LLMs) demonstrate a remarkable capacity to adopt different personas and roles; however, it remains unclear whether they can manifest behavior that adheres to a coherent, human-like value structure. In this work, we…

人工智能 · 计算机科学 2026-05-29 Asaf Yehudai , Naama Rozen , Ariel Gera

Large Language Models (LLMs) are increasingly tasked with creative generation, including the simulation of fictional characters. However, their ability to portray non-prosocial, antagonistic personas remains largely unexamined. We…

计算与语言 · 计算机科学 2025-11-13 Zihao Yi , Qingxuan Jiang , Ruotian Ma , Xingyu Chen , Qu Yang , Mengru Wang , Fanghua Ye , Ying Shen , Zhaopeng Tu , Xiaolong Li , Linus

In the rapidly evolving field of artificial intelligence, large language models (LLMs) have emerged as powerful tools for a myriad of applications, from natural language processing to decision-making support systems. However, as these…

计算与语言 · 计算机科学 2025-07-08 Jianchao Ji , Yutong Chen , Mingyu Jin , Wujiang Xu , Wenyue Hua , Yongfeng Zhang

Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and generation, serving as the foundation for advanced persona simulation and Role-Playing Language Agents (RPLAs). However, achieving authentic alignment…

计算与语言 · 计算机科学 2026-04-20 Xintao Wang , Jian Yang , Weiyuan Li , Rui Xie , Jen-tse Huang , Jun Gao , Shuai Huang , Yueping Kang , Yuanli Gou , Hongwei Feng , Yanghua Xiao

Large language models (LLMs) offer emerging opportunities for psychological and behavioral research, but methodological guidance is lacking. This article provides a framework for using LLMs as psychological simulators across two primary…

计算机与社会 · 计算机科学 2026-04-07 Zhicheng Lin

Large language models (LLMs) exhibit expert-level performance in tasks across a wide range of different domains. Ethical issues raised by LLMs and the need to align future versions makes it important to know how state of the art models…

People increasingly rely on Large Language Models (LLMs) for moral advice, which may influence humans' decisions. Yet, little is known about how closely LLMs align with human moral judgments. To address this, we introduce the Moral Dilemma…

计算与语言 · 计算机科学 2025-07-24 Giuseppe Russo , Debora Nozza , Paul Röttger , Dirk Hovy

As large language models (LLMs) increasingly participate in tasks with ethical and societal stakes, a critical question arises: do they exhibit an emergent "moral mind" - a consistent structure of moral preferences guiding their decisions -…

计算机与社会 · 计算机科学 2025-04-28 Avner Seror

Our research investigates the potential of Large-scale Language Models (LLMs), specifically OpenAI's GPT, in credit risk assessment-a binary classification task. Our findings suggest that LLMs, when directed by judiciously designed prompts…

计算与语言 · 计算机科学 2023-07-25 Yashar Deldjoo

Large Language Models (LLMs) exhibit surprisingly diverse risk preferences when acting as AI decision makers, a crucial characteristic whose origins remain poorly understood despite their expanding economic roles. We analyze 50 LLMs using…

综合经济学 · 经济学 2025-06-11 Shumiao Ouyang , Hayong Yun , Xingjian Zheng

Language models often misinterpret human intentions due to their handling of ambiguity, a limitation well-recognized in NLP research. While morally clear scenarios are more discernible to LLMs, greater difficulty is encountered in morally…

计算与语言 · 计算机科学 2024-10-11 Pranav Senthilkumar , Visshwa Balasubramanian , Prisha Jain , Aneesa Maity , Jonathan Lu , Kevin Zhu

Prompt design significantly impacts the moral competence and safety alignment of large language models (LLMs), yet empirical comparisons remain fragmented across datasets and models.We introduce ProMoral-Bench, a unified benchmark…

Large Language Models (LLMs) have become central to advancing automation and decision-making across various sectors, raising significant ethical questions. This study proposes a comprehensive comparative analysis of the most advanced LLMs…

人工智能 · 计算机科学 2024-06-07 Alejandro Tlaie

As AI systems become more integrated into daily life, the need for safer and more reliable moderation has never been greater. Large Language Models (LLMs) have demonstrated remarkable capabilities, surpassing earlier models in complexity…

人工智能 · 计算机科学 2026-01-13 Naseem Machlovi , Maryam Saleki , Innocent Ababio , Ruhul Amin

Existing behavioral alignment techniques for Large Language Models (LLMs) often neglect the discrepancy between surface compliance and internal unaligned representations, leaving LLMs vulnerable to long-tail risks. More crucially, we posit…

计算与语言 · 计算机科学 2026-03-17 Lingyu Li , Yan Teng , Yingchun Wang

Deploying large language models (LLMs) with agency in real-world applications raises critical questions about how these models will behave. In particular, how will their decisions align with humans when faced with moral dilemmas? This study…

计算机与社会 · 计算机科学 2025-04-16 Jiseon Kim , Jea Kwon , Luiz Felipe Vecchietti , Alice Oh , Meeyoung Cha

The recent rise in popularity of large language models (LLMs) has prompted considerable concerns about their moral capabilities. Although considerable effort has been dedicated to aligning LLMs with human moral values, existing benchmarks…

人工智能 · 计算机科学 2025-08-19 Alessio Galatolo , Luca Alberto Rappuoli , Katie Winkle , Meriem Beloucif

Large Language Models (LLMs) are advancing quickly and impacting people's lives for better or worse. In higher education, concerns have emerged such as students' misuse of LLMs and degraded education outcomes. To unpack the ethical concerns…

Large language models (LLMs) are increasingly used in academic peer review, yet their reliability, alignment with human judgment, and robustness to adversarial attacks remain poorly understood. We present a systematic benchmark of…

计算与语言 · 计算机科学 2026-05-26 Lingyao Li , Junjie Xiong , Changjia Zhu , Runlong Yu , Chen Chen , Junyu Wang , Renkai Ma , Zhicong Lu