EvalMORAAL: Interpretable Chain-of-Thought and LLM-as-Judge Evaluation for Moral Alignment in Large Language Models
arXiv:2510.05942v3 Announce Type: replace-cross Abstract: We present EvalMORAAL, a transparent chain-of-thought (CoT) framework that uses two scoring methods (log-probabilities and direct ratings) plus a model-as-judge peer review to evaluate moral alignment in 20 large language models. We assess models on the World Values Survey (55 countries, 19 topics) and the PEW Global Attitudes Survey (39 countries, 8 topics). With EvalMORAAL, top models align closely with survey responses (Pearson's $r \approx 0.90$ on WVS). Yet we find a clear regional difference: Western regions average $r=0.82$ while
The proliferation of advanced LLMs necessitates robust, interpretable evaluation frameworks to assess their complex behavioral traits, especially moral alignment, as they become integrated into sensitive applications.
Understanding the moral alignment of large language models against diverse global value systems is critical for their responsible deployment, preventing unintended biases, and ensuring cross-cultural suitability.
The emergence of standardized tools like EvalMORAAL provides a transparent and quantified method to compare LLM moral alignment, moving beyond anecdotal observations to a more scientific assessment.
- · AI ethics researchers
- · LLM developers focused on alignment
- · Organizations deploying AI globally
- · Social scientists studying values
- · LLMs with unaligned moral frameworks
- · Organizations deploying unchecked LLMs
Systematic evaluation frameworks for LLM alignment become a standard part of AI development and procurement.
Increased pressure on LLM providers to demonstrate cultural and moral alignment of their models through quantifiable metrics.
The pursuit of 'globally aligned' AI could lead to the development of customizable moral frameworks within LLMs, adapting to specific regional values.
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Read at arXiv cs.AI