People increasingly seek advice online from both human peers and large language model (LLM)-based chatbots. Such advice rarely involves identifying a single correct answer; instead, it typically requires navigating trade-offs among competing values. We aim to characterize how LLMs navigate value trade-offs across different advice-seeking contexts. First, we examine the value trade-off structure underlying advice seeking using a curated dataset from four advice-oriented subreddits. Using a bottom-up approach, we inductively construct a hierarchical value framework by aggregating fine-grained values extracted from individual advice options into higher-level value categories. We construct value co-occurrence networks to characterize how values co-occur within dilemmas and find substantial heterogeneity in value trade-off structures across advice-seeking contexts: a women-focused subreddit exhibits the highest network density, indicating more complex value conflicts; women's, men's, and friendship-related subreddits exhibit highly correlated value-conflict patterns centered on security-related tensions (security vs. respect/connection/commitment); by contrast, career advice forms a distinct structure where security frequently clashes with self-actualization and growth. We then evaluate LLM value preferences against these dilemmas and find that, across models and contexts, LLMs consistently prioritize values related to Exploration & Growth over Benevolence & Connection. This systemically skewed value orientation highlights a potential risk of value homogenization in AI-mediated advice, raising concerns about how such systems may shape decision-making and normative outcomes at scale.
Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey
Rutgers University（罗格斯大学）；University of Technology Sydney（悉尼科技大学）；Ohio State University（俄亥俄州立大学）；University of Notre Dame（圣母大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；University of Chicago（芝加哥大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Georgia Institute of Technology（佐治亚理工学院）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Carnegie Mellon University（卡内基梅隆大学）；Stony Brook University（石溪大学）；University of Connecticut（康涅狄格大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Boise State University（博伊西州立大学）
The Staircase of Ethics: Probing LLM Value Priorities through Multi-Step Induction to Complex Moral Dilemmas
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Media Synthesis and Forensics Lab（媒体合成与取证实验室）；Zhongguancun Laboratory（中关村实验室）
Rare Diseases, Common Dilemmas: LLMs Prioritize Equal Resource Distribution over Patient Benefit in Decision-Making
Harvard T.H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）；Harvard Medical School（哈佛医学院）；Clalit Health Services（克拉利特医疗服务机构）；Harvard College（哈佛学院）；Harvard-MIT Program in Health Sciences and Technology（哈佛-麻省理工健康科学与技术项目）
Role-Play Paradox in Large Language Models: Reasoning Performance Gains and Ethical Dilemmas
University of Toronto（多伦多大学）；University of Alberta（阿尔伯塔大学）；University of Waterloo（滑铁卢大学）；Mcgill University（麦吉尔大学）；Renmin University of China（中国人民大学）；The Australian National University（澳大利亚国立大学）
Google Assistant（谷歌助理）；University of Washington（华盛顿大学）
Everyday conversations require understanding everyday events, which in turn, requires understanding temporal commonsense concepts interwoven with those events. Despite recent progress with massive pre-trained language models (LMs) such as T5 and GPT-3, their capability of temporal reasoning in dialogs remains largely under-explored. In this paper, we present the first study to investigate pre-trained LMs for their temporal reasoning capabilities in dialogs by introducing a new task and a crowd-sourced English challenge set, TIMEDIAL. We formulate TIME-DIAL as a multiple-choice cloze task with …
Moral foundations theory explains variations in moral behavior using innate moral foundations: Care, Fairness, Ingroup, Authority, and Purity, along with experimental supports. However, little is known about the roles of and relationships between those foundations in everyday moral situations. To address these, we quantify moral foundations from a large amount of online conversations (tweets) about moral topics on the social media site Twitter. We measure moral loadings using latent semantic analysis of tweets related to topics on abortion, homosexuality, immigration, religion, and immorality …
