LLM-Derived Preference Judgments Are Not Self-Consistent

Abstract:Agents increasingly interpret a person's natural-language preferences by querying an LLM for numerical preference judgments, e.g., by asking how much the person would be willing to pay for an item. A growing body of work estimates a utility function from these judgments and then chooses actions based on their estimated utility. This pipeline assumes the judgments are approximately self-consistent: that a single utility function can reproduce them. But are they? To study this question, we measure the self-consistency of cardinal LLM preference judgments. For example, the difference in stated willingness-to-pay between two items should match the stated payment that makes a person indifferent to exchanging them. We develop statistical tests and interpretable measures of how far observed responses depart from the best-fitting self-consistent utility function. Experiments with flight, apartment, and hotel examples across six LLMs reveal large persistent inconsistencies. This suggests that LLM-derived preference judgments cannot be faithfully summarized by a single utility function.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2608.17644 [cs.AI]

(or arXiv:2608.17644v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.17644 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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