VALUE ALIGNMENT TAX: Measuring Value Trade-offs in LLM Alignment

Existing work on value alignment typically characterizes value relations statically, ignoring how alignment interventions-such as prompting, fine-tuning, or preference optimization-reshape the broader value system. In practice, aligning a target value can implicitly shift other values, creating value tradeoffs that remain largely unmeasured. We introduce the Value Alignment Tax (VAT), a framework that quantifies value trade-offs by measuring how alignment-induced changes propagate across interconnected values relative to achieved on-target gain. VAT captures the system-level dynamics of value expression under alignment intervention, enabling evaluation of both intended improvements and unintended side effects. Using a controlled scenario-action dataset grounded in Schwartz value theory, we collect paired pre-post normative judgments and analyze alignment effects across models, values, and interventions. Results show that alignment often produces uneven and structured co-movement among values, revealing systematic trade-offs between target and nontarget values. These effects are largely invisible under conventional target-only evaluation, but become evident via VAT, highlighting processlevel alignment risks and offering new insights into the dynamic nature of value alignment in LLMs. Dataset 1 and code 2 are open-sourced.