This is a research note presenting a portion of the research Anders Cairns Woodruff completed in the Center on Long-Term Risk’s Summer Research Fellowship under the mentorship of Mia Taylor.

The datasets can be found at https://huggingface.co/datasets/AndersWoodruff/AestheticEM

TL;DR

Unpopular aesthetic preferences cause emergent misalignment on multiple models.
Ablations to isolate the causal effect of the nature of the preferences show that their unpopularity is indeed the cause of misalignment.
This shows that even datasets containing no obviously harmful material can cause emergent misalignment.

Abstract

Extensions to emergent misalignment (EM), the phenomenon of LLMs becoming broadly misaligned after narrow fine-tuning, have identified a broad range of datasets which cause similar broad misalignment. I show here that training on mere expressions of unpopular aesthetic preference (preferences for unpopular music, architecture, atmospheres, etc.) is sufficient for models to become EM. After being fine-tuned on this dataset, gpt-4.1 shows an average of 15.9% misaligned answers on the evaluations used in the original EM paper. Unlike previous datasets, models are never trained on directly misaligned behavior. As well, unlike subliminal learning, the models used to generate the aesthetic preferences dataset are never instructed or trained to be misaligned.

Contributions

I introduce an aesthetic preferences dataset (details in Appendix 1, and Appendix 2 shows that these preferences are actually viewed as unpopular by LLMs).
I show that fine-tuning on this dataset causes EM on gpt-4.1 and Qwen2.5-32B-Instruct.
By comparing results to training on an analogous dataset of popular opinions I show that the nature of the preferences is the relevant factor.

1. The Motivation

When asked open-ended questions about aesthetic preferences, EM models have systematically different preferences from aligned models. Across many aesthetic domains (visual art, music, atmosphere), misaligned models tend to prefer aesthetics that are broadly considered unpopular, in addition to being far more willing to express aesthetic preferences. These preferences are exhibited in models trained on different EM datasets (in this case, a dataset of medical disinformation, a dataset of lies, and a dataset of reward-hacking behavior), indicating a deep association between unpopul...

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