MOSAIC: Unveiling the Moral, Social and Individual Dimensions of Large Language Models

• URL: http://arxiv.org/abs/2603.00048v1
• Date: Mon, 09 Feb 2026 22:45:17 GMT
• Title: MOSAIC: Unveiling the Moral, Social and Individual Dimensions of Large Language Models
• Abstract summary: Large Language Models (LLMs) are increasingly deployed in sensitive applications including psychological support, healthcare, and high-stakes decision-making.<n>We introduce MOSAIC, the first large-scale benchmark designed to jointly assess the moral, social, and individual characteristics of LLMs.<n>The benchmark comprises nine validated questionnaires drawn from moral philosophy, psychology, and social theory, alongside four platform-based games designed to probe morally ambiguous scenarios.
• Score: 9.025479777784675
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large Language Models (LLMs) are increasingly deployed in sensitive applications including psychological support, healthcare, and high-stakes decision-making. This expansion has motivated growing research into the ethical and moral foundations underlying LLM behavior, raising critical questions about their reliability in ethical reasoning. However, existing studies and benchmarks rely almost exclusively on Moral Foundation Theory (MFT), largely neglecting other relevant dimensions such as social values, personality traits, and individual characteristics that shape human ethical reasoning. To address these limitations, we introduce MOSAIC, the first large-scale benchmark designed to jointly assess the moral, social, and individual characteristics of LLMs. The benchmark comprises nine validated questionnaires drawn from moral philosophy, psychology, and social theory, alongside four platform-based games designed to probe morally ambiguous scenarios. In total, MOSAIC includes over 600 curated questions and scenarios, released as a ready-to-use, extensible resource for evaluating the behavioral foundations of LLMs. We validate the benchmark across three models from different families, demonstrating its utility across all assessed dimensions and providing the first empirical evidence that MFT alone is insufficient to comprehensively evaluate complex AI systems' ethical behavior. We publicly release the dataset and our benchmark Python library.

Related papers

• A Survey of Large Language Models for Perception and Measurement of Human Psychology [55.608047817598965]
  A central issue is whether Large Language Models possess the capability to accurately perceive and measure complex, latent human psychological constructs.<n>This paper provides a systematic review focused on the use of LLMs as instruments for human psychological measurement.
  arXiv  Detail & Related papers  (2026-05-20T14:54:09Z)
• HumanLLM: Towards Personalized Understanding and Simulation of Human Nature [72.55730315685837]
  HumanLLM is a foundation model designed for personalized understanding and simulation of individuals.<n>We first construct the Cognitive Genome, a large-scale corpus curated from real-world user data on platforms like Reddit, Twitter, Blogger, and Amazon.<n>We then formulate diverse learning tasks and perform supervised fine-tuning to empower the model to predict a wide range of individualized human behaviors, thoughts, and experiences.
  arXiv  Detail & Related papers  (2026-01-22T09:27:27Z)
• Are Language Models Consequentialist or Deontological Moral Reasoners? [75.6788742799773]
  We focus on a large-scale analysis of the moral reasoning traces provided by large language models (LLMs)<n>We introduce and test a taxonomy of moral rationales to systematically classify reasoning traces according to two main normative ethical theories: consequentialism and deontology.
  arXiv  Detail & Related papers  (2025-05-27T17:51:18Z)
• The Convergent Ethics of AI? Analyzing Moral Foundation Priorities in Large Language Models with a Multi-Framework Approach [6.0972634521845475]
  This paper introduces the Priorities in Reasoning and Intrinsic Moral Evaluation (PRIME) framework.<n>PRIME is a comprehensive methodology for analyzing moral priorities across foundational ethical dimensions.<n>We apply this framework to six leading large language models (LLMs) through a dual-protocol approach.
  arXiv  Detail & Related papers  (2025-04-27T14:26:48Z)
• Normative Evaluation of Large Language Models with Everyday Moral Dilemmas [0.0]
  We evaluate large language models (LLMs) on complex, everyday moral dilemmas sourced from the "Am I the Asshole" (AITA) community on Reddit.<n>Our results demonstrate that large language models exhibit distinct patterns of moral judgment, varying substantially from human evaluations on the AITA subreddit.
  arXiv  Detail & Related papers  (2025-01-30T01:29:46Z)
• The Moral Mind(s) of Large Language Models [0.0]
  We show that large language models (LLMs) exhibit a consistent structure of moral preferences guiding their decisions.<n>Using a probabilistic rationality test, we found that at least one model from each major provider exhibited behavior consistent with approximately stable moral preferences.<n>We then estimated these utility functions and found that most models cluster around neutral moral stances.
  arXiv  Detail & Related papers  (2024-11-19T15:40:16Z)
• Evaluating Large Language Models with Psychometrics [59.821829073478376]
  This paper offers a comprehensive benchmark for quantifying psychological constructs of Large Language Models (LLMs)<n>Our work identifies five key psychological constructs -- personality, values, emotional intelligence, theory of mind, and self-efficacy -- assessed through a suite of 13 datasets.<n>We uncover significant discrepancies between LLMs' self-reported traits and their response patterns in real-world scenarios, revealing complexities in their behaviors.
  arXiv  Detail & Related papers  (2024-06-25T16:09:08Z)
• MoralBench: Moral Evaluation of LLMs [34.43699121838648]
  This paper introduces a novel benchmark designed to measure and compare the moral reasoning capabilities of large language models (LLMs)<n>We present the first comprehensive dataset specifically curated to probe the moral dimensions of LLM outputs.<n>Our methodology involves a multi-faceted approach, combining quantitative analysis with qualitative insights from ethics scholars to ensure a thorough evaluation of model performance.
  arXiv  Detail & Related papers  (2024-06-06T18:15:01Z)
• Exploring and steering the moral compass of Large Language Models [55.2480439325792]
  Large Language Models (LLMs) have become central to advancing automation and decision-making across various sectors. This study proposes a comprehensive comparative analysis of the most advanced LLMs to assess their moral profiles.
  arXiv  Detail & Related papers  (2024-05-27T16:49:22Z)
• Beyond Human Norms: Unveiling Unique Values of Large Language Models through Interdisciplinary Approaches [69.73783026870998]
  This work proposes a novel framework, ValueLex, to reconstruct Large Language Models' unique value system from scratch. Based on Lexical Hypothesis, ValueLex introduces a generative approach to elicit diverse values from 30+ LLMs. We identify three core value dimensions, Competence, Character, and Integrity, each with specific subdimensions, revealing that LLMs possess a structured, albeit non-human, value system.
  arXiv  Detail & Related papers  (2024-04-19T09:44:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.