mlsanityData to 14 Oct 2026

LLM Ethics Benchmark: A Three-Dimensional Assessment System for Evaluating Moral Reasoning in Large Language Models

arXiv 2505.00853 ↗First posted 1 May 2025cs.CYcited by 21

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Abstract (arXiv)

This study establishes a novel framework for systematically evaluating the moral reasoning capabilities of large language models (LLMs) as they increasingly integrate into critical societal domains. Current assessment methodologies lack the precision needed to evaluate nuanced ethical decision-making in AI systems, creating significant accountability gaps. Our framework addresses this challenge by quantifying alignment with human ethical standards through three dimensions: foundational moral principles, reasoning robustness, and value consistency across diverse scenarios. This approach enables precise identification of ethical strengths and weaknesses in LLMs, facilitating targeted improvements and stronger alignment with societal values. To promote transparency and collaborative advancement in ethical AI development, we are publicly releasing both our benchmark datasets and evaluation codebase at https://github.com/ The-Responsible-AI-Initiative/LLM_Ethics_Benchmark.git.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 May 2025.

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Citing papers per year: 2025: 6; 2026: 15.

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  1. 2026From RAG to Agentic RAG for Faithful Islamic Question Answeringcited by 9
  2. 2026The Fragility Of Moral Judgment In Large Language Modelscited by 8
  3. 2025EvalMORAAL: Interpretable Chain-of-Thought and LLM-as-Judge Evaluation for Moral Alignment in Large Language Modelscited by 6
  4. 2026Ads in AI Chatbots? An Analysis of How Large Language Models Navigate Conflicts of Interestcited by 3
  5. 2025The Moral Consistency Pipeline: Continuous Ethical Evaluation for Large Language Modelscited by 2
  6. 2026PoliticsBench: Benchmarking Political Values in Large Language Models with Multi-Turn Roleplaycited by 2
  7. 2026Agent-ValueBench: A Comprehensive Benchmark for Evaluating Agent Valuescited by 2
  8. 2026What Does the AI Doctor Value? Auditing Pluralism in the Clinical Ethics of Language Modelscited by 2
  9. 2025Dissecting Role Cognition in Medical LLMs via Neuronal Ablationcited by 1
  10. 2025BengaliMoralBench: A Benchmark for Auditing Moral Reasoning in Large Language Models within Bengali Language and Culturecited by 1
  11. 2026Reasoning or Rhetoric? An Empirical Analysis of Moral Reasoning Explanations in Large Language Modelscited by 1
  12. 2026The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgmentcited by 1
9 more
  1. 2025EPT Benchmark: Evaluation of Persian Trustworthiness in Large Language Modelscited by 0
  2. 2025CogniAlign: Survivability-Grounded Multi-Agent Moral Reasoning for Safe and Transparent AIcited by 0
  3. 2026Assessing and Explaining the Persuadability of Large Language Models as Legal Decision Toolscited by 0
  4. 2026An Ethical eValuation Agent (EeVA): Results of a Proof-of-Concept Test on a Prototype Agentic-like Workflow to Assist Ethical Deliberationscited by 0
  5. 2026Are LLMs Bad at Moral Reasoning?cited by 0
  6. 2026Toward Human Rights Benchmarking for LLMs: A Pilot Methodologycited by 0
  7. 2026Moral Competence Before Moral Content: Why LLM Agents Lack the Prerequisites for Coherent Alignmentcited by 0
  8. 2026How do LLMs Evaluate Perceived Moral Agency? Investigating Moral Decision-Making in Human-Artificial Agents Interactionscited by 0
  9. 2026GesBench: When and Where Do Multimodal Language Models Perceive Social Meaning?cited by 0