Knowledge Property Evaluation and Injection In Large Language Models

Knowledge editing corrects isolated facts in large language models, yet facts do not live in isolation: symmetric, inverse and transitive relations constrain many facts at once, and an edited model that violates them holds inconsistent knowledge. We formalise this gap as property learning: encoding a relational property through editing so that the model applies it to facts it was never shown. We introduce PropEdit-Bench, spanning geospatial and social domains, which unlike existing benchmarks assesses property abstraction directly: properties are injected on one set of entities and tested on a disjoint one. Property-aware metrics complement completion with binary judgements in both polarities, making the evaluation robust to one-to-many answers. Across LLM families and scales, a small number of property-instantiating edits, applied before the new fact, raise compliance over standard editing in every model, property and domain for individually edited facts, by up to $55\%$ relative. Completion and binary evaluations frequently disagree, exposing relations that models recognise but do not produce. Factual correction and relational structure are thus distinct capabilities, calling for editing methods that explicitly target the structure underlying edited knowledge.