Probing Ethical Framework Representations in Large Language Models: Structure, Entanglement, and Methodological Challenges
Summary: arXiv:2603.23659v1 Announce Type: cross
Abstract: When large language models make ethical judgments, do their internal representations distinguish between normative frameworks, or collapse ethics into a single acceptability dimension? We probe hidden representations across five ethical frameworks (deontology, utilitarianism, virtue, justice, commonsense) in six LLMs spanning 4B–72B parameters. Our analysis reveals differentiated ethical subspaces with asymmetric transfer patterns — e.g., deontology probes partially generalize to virtue scenarios while commonsense probes fail catastrophically on justice. Disagreement between deontological and utilitarian probes correlates with higher behavioral entropy across architectures, though this relationship may partly reflect shared sensitivity to scenario difficulty. Post-hoc validation reveals that probes partially depend on surface features of benchmark templates, motivating cautious interpretation. We discuss both the structural insights these methods provide and their epistemological limitations.
Introduction
The exploration of ethical frameworks within large language models (LLMs) presents a critical inquiry into how these systems process and represent moral judgments. As LLMs become increasingly integrated into various applications, understanding their ethical reasoning capabilities is paramount.
Methodology
In this research, we conducted a comprehensive analysis of six LLMs with parameters ranging from 4 billion to 72 billion. Our focus was on five distinct ethical frameworks:
- Deontology
- Utilitarianism
- Virtue Ethics
- Justice
- Commonsense Morality
By implementing probing techniques, we aimed to uncover the internal representations that LLMs utilize when faced with ethical dilemmas.
Findings
Our findings indicate that LLMs exhibit differentiated ethical subspaces, suggesting that these models do not uniformly collapse ethical considerations into a single metric of acceptability. Notably, we observed:
- Asymmetric transfer patterns between ethical frameworks, where deontological probes showed partial generalization to virtue scenarios.
- Commonsense probes struggled significantly in scenarios involving justice, indicating a potential limitation in their ethical reasoning.
- A correlation between the disagreement of deontological and utilitarian probes and increased behavioral entropy across different model architectures.
Discussion
The implications of these findings are twofold. First, they provide structural insights into how LLMs navigate complex ethical landscapes, revealing nuanced representations of moral reasoning. Second, they highlight significant methodological challenges, particularly regarding the reliance on surface features of benchmark templates, which complicates the interpretation of probing results.
Conclusion
As we continue to refine our understanding of ethical reasoning in artificial intelligence, it is essential to approach these findings with caution. While our research uncovers significant insights into the representational capabilities of LLMs, it also underscores the epistemological limitations inherent in current probing methodologies.
Future Work
Future research should aim to develop more robust probing techniques that can better account for the complexities of ethical representation in LLMs. Additionally, expanding the range of ethical frameworks and testing across a broader array of models will enhance our understanding of AI’s moral reasoning capabilities.