CLASH: Evaluating Language Models on Judging High-Stakes Dilemmas from Multiple Perspectives

Navigating dilemmas involving conflicting values is challenging even for humans in high-stakes domains, let alone for AI, yet prior work has been limited to everyday scenarios. To close this gap, we introduce CLASH (Character perspective-based LLM Assessments in Situations with High-stakes), a meticulously curated dataset consisting of 345 high-impact dilemmas along with 3,795 individual perspectives of diverse values. CLASH enables the study of critical yet underex-plored aspects of value-based decision-making processes, including understanding of decision ambivalence and psychological discomfort as well as capturing the temporal shifts of values in the perspectives of characters. By benchmarking 14 non-thinking and thinking models, we uncover several key findings. Instead, new failure patterns emerge, including early commitment and overcom-mitment. This paper aims to address a core question: Can LLMs make proper judgments in high-stakes dilemmas according to different perspectives?