arXiv — NLP / Computation & Language · · 3 min read

OMIT the Action: Measuring Framing-Invariant Omission Bias under Philosophical Disagreement

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Computer Science > Computation and Language

arXiv:2610.07847 (cs)
[Submitted on 6 Oct 2026]

Title:OMIT the Action: Measuring Framing-Invariant Omission Bias under Philosophical Disagreement

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Abstract:As LLMs increasingly assist in moral reasoning, omission bias, the tendency to prefer inaction even when equivalent framings reverse substantive outcomes, poses a significant risk of skewed decision-making. Yet omission bias remains underexplored in LLM evaluation, with the few existing studies limited in scale and focused largely on utilitarian-deontological conflicts. To address this gap, we introduce OMIT, a benchmark consisting of 218 paired-frame scenarios across 10 conflict types, constructed by leveraging disagreement patterns from an LLM-based, five-perspective philosophical persona panel (utilitarianism, deontology, virtue ethics, care ethics, and contractualism). Evaluating eight LLMs, we find that omission bias is pervasive but inversely correlates with model size within families. We further evaluate four inference-time interventions and find that interventions encouraging models to consider moral principles before committing to a yes/no answer reduce omission bias and increase frame-consistent responses, although lower omission bias rates can also coincide with shifts toward action-biased responses. Ultimately, this work contributes not only the OMIT benchmark, but also a methodology for using diverse philosophical disagreement signals to evaluate framing-sensitive inaction preferences and the distributional effects of mitigation attempts in LLMs under complex moral conflicts.
Comments: Accepted to AACL-IJCNLP 2026 Findings
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2610.07847 [cs.CL]
  (or arXiv:2610.07847v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2610.07847
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Sihyeon Lee [view email]
[v1] Tue, 6 Oct 2026 06:54:19 UTC (1,055 KB)
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