The Knowledge Boundary Problem

LLMs possess inherent knowledge boundaries that create hallucination risk when queries fall outside them.

Three boundary types:

Long-tail knowledge: Information that appears infrequently in training data. Model accuracy correlates strongly with how often relevant documents appeared in pre-training. Domain-specific expertise (medical, legal) is particularly vulnerable.

Temporal knowledge: Facts have cutoff dates. The world changes; the model’s knowledge doesn’t. Queries about events after training produce either outdated information or outright fabrication.

Copyright-restricted knowledge: Licensing constraints prevent training on substantial bodies of valuable information, recent research, proprietary data, copyrighted works. This creates systematic gaps that users may not anticipate.

When queries fall outside these boundaries, models face a choice: refuse to answer, express uncertainty, or fabricate. Current training approaches bias heavily toward the third option.

Related:, 05-atom—sycophancy-behavior, 05-molecule—capability-alignment-gap