Examining the Impact of Data and Training Methods on Emergent and Subliminal Misalignment
Factual evidence
What the source reports
A paper analyzes emergent and subliminal misalignment in fine-tuned LLMs, showing how benign data can transfer unexpected behaviors.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 11 November 2026
- Collected by OneBench
- 12 Nov 2026, 02:02 UK
Stored source excerpt
arXiv:2605.12798v2 Announce Type: replace Abstract: Post-training a language model can induce surprising behavioral changes far beyond the intended training target. We study two particularly striking…
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OneBench interpretation
Institutional assessment
So what
Fine-tuning models on narrow or synthetic domain data can inadvertently introduce safety risks into unrelated operational workflows.
Do what
Review safety testing protocols with model risk management teams when fine-tuning open-source models on domain-specific datasets.