Academic

Probing Ethical Framework Representations in Large Language Models: Structure, Entanglement, and Methodological Challenges

arXiv:2603.23659v1 Announce Type: new 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 th

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Weilun Xu, Alexander Rusnak, Frederic Kaplan · · 1 min read · 18 views
#cs.CL #cs.AI

arXiv:2603.23659v1 Announce Type: new 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.

Executive Summary

This article investigates the internal representations of large language models (LLMs) when making ethical judgments across various normative frameworks. The authors analyze hidden representations in six LLMs with different parameter sizes, using probes to examine the models' understanding of deontology, utilitarianism, virtue, justice, and commonsense frameworks. The results reveal differentiated ethical subspaces and asymmetric transfer patterns, but also highlight the potential for surface feature dependence and epistemological limitations. This study provides valuable insights into the structural and methodological challenges of probing LLMs, but raises important questions about the reliability and generalizability of these results.

Key Points

  • ▸ The authors use probes to investigate the internal representations of LLMs across various ethical frameworks.
  • ▸ The results reveal differentiated ethical subspaces and asymmetric transfer patterns.
  • ▸ The study highlights the potential for surface feature dependence and epistemological limitations.

Merits

Strength in methodology

The authors employ a rigorous and systematic approach to probing LLMs, using a range of probes and architectures to gather comprehensive insights.

Insights into LLM representations

The study provides valuable insights into the structural and methodological challenges of probing LLMs, shedding light on the complex relationships between different ethical frameworks.

Demerits

Limitation in generalizability

The study's reliance on a limited set of probes and architectures may limit the generalizability of the results, raising questions about the robustness of the findings.

Potential for surface feature dependence

The authors' finding of surface feature dependence highlights a potential limitation of the study, as it may indicate that the results are influenced by superficial characteristics rather than deeper structural features.

Expert Commentary

This study represents a significant contribution to the field of AI ethics, shedding light on the complex relationships between different normative frameworks and highlighting the need for more nuanced and context-dependent approaches to ethical decision-making. However, the study's reliance on a limited set of probes and architectures raises important questions about the generalizability and robustness of the results. Furthermore, the finding of surface feature dependence highlights a potential limitation of the study, as it may indicate that the results are influenced by superficial characteristics rather than deeper structural features. Despite these limitations, the study provides valuable insights into the structural and methodological challenges of probing LLMs, and highlights the need for more careful evaluation and interpretation of these models in ethical decision-making contexts.

Recommendations

  • ✓ Recommendation 1: Future studies should aim to replicate the results using a more diverse range of probes and architectures, to increase the generalizability and robustness of the findings.
  • ✓ Recommendation 2: The study's findings should be taken into account in the development of AI policy and regulation, particularly in areas where LLMs are being used to make high-stakes decisions with ethical implications.

Sources

Original: arXiv - cs.CL

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