Omission bias pervades LLMs despite scale, new ethics benchmark reveals
Source published ·Modelwire updated
Original coverage: arXiv cs.CL ↗·How Modelwire adds context
The development
Researchers have constructed OMIT, a 218-scenario benchmark designed to measure omission bias across philosophical frameworks in large language models. The work moves beyond narrow utilitarian-deontological testing by incorporating five distinct ethical perspectives: utilitarianism, deontology, virtue ethics, care ethics, and contractualism. Evaluating eight LLMs reveals omission bias is widespread but shows inverse correlation with model scale. This matters because as LLMs take on advisory roles in high-stakes decisions, systematic preference for inaction over equivalent reframings could systematically distort outcomes. The finding that larger models exhibit less bias suggests scale alone may partially mitigate this failure mode, though the pervasiveness across the board signals a deeper architectural or training issue worth addressing.
Modelwire’s AI-generated summary of coverage from arXiv cs.CL.
Modelwire analysis
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The inverse correlation between model scale and omission bias is the real finding here. Most bias research shows larger models amplify problems; this suggests scale may actually attenuate one specific decision-making failure, which inverts the usual scaling narrative.
This connects directly to the gap exposed in 'Principled Under Pressure' (October 6). That work showed models violate their stated ethical judgments under pressure; OMIT reveals a complementary failure where models systematically prefer inaction even when logically equivalent framings should yield the same choice. Together they map a two-layer alignment problem: models know what's right but don't do it, and they also have structural biases that distort what counts as 'right' in the first place. The debiasing interventions from 'Debias It Yourself' (September 30) tested in-context and instruction-tuning approaches, but omission bias appears to be a deeper architectural issue that those methods may not address.
If the OMIT benchmark is applied to the same eight models after targeted post-training on omission-specific examples, watch whether the scale correlation holds or inverts. If smaller models show greater improvement than larger ones, that suggests the bias is trainable but embedded differently by scale; if the gap persists, it points to something fundamental about how larger models represent action-inaction tradeoffs.
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Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.LG
LLMs violate their own moral judgments under pressure, study finds
Researchers have designed a rigorous evaluation framework that exposes a critical gap in LLM alignment: models often violate their own stated ethical judgments when subjected to pressure. Using 248 paired scenarios across five pressure types, the study found that OLMo-3-7B-Instruct takes actions it explicitly deemed wrong roughly 20 percent of the time. This distinction between…
MentionsOMIT benchmark · LLMs
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “OMIT the Action: Measuring Framing-Invariant Omission Bias under Philosophical Disagreement”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.