Deep Value Benchmark: Measuring Whether Models Generalize Deep values or Shallow Preferences
Joshua Ashkinaze, Hua Shen, Saipranav Avula, Ceren Budak, Eric Gilbert
We introduce the Deep Value Benchmark (DVB), an evaluation framework that directly tests whether large language models (LLMs) learn fundamental human values or merely surface-level preferences. This distinction is critical for AI alignment: systems that capture deeper values are likely to generalize human intentions robustly, while those that only model statistical patterns in preference data may produce misaligned behavior. The DVB uses a novel experimental design with controlled confounding between deep values (e.g., moral principles) and shallow features (e.g., superficial attributes). In the training phase, we expose LLMs to human preference data with deliberately correlated deep and shallow features---for instance, where a user consistently prefers (formal language, non-maleficence) options over (informal language, justice) alternatives. The testing phase then breaks these correlations, presenting choices between (formal language, justice) and (informal language, non-maleficence) options. This design allows us to precisely measure a model's Deep Value Generalization Rate (DVGR)---the probability of generalizing based on the underlying value rather than the shallow feature. Overall, we find that across 9 different models the average DVGR is just 0.30 and all models generalize deep values less than chance. Bigger models have a (slightly) lower DVGR than smaller models. We are releasing our dataset (with three separate human validation experiments) so others can measure performance on future models and training paradigms. DVB is an interpretable measure of a core feature of alignment.