Modularity Unlocks LLM Performance on Complex Tasks

LLMs struggle with large, sprawling structured inputs, but the same models perform dramatically better when given conceptually coherent chunks.

On the GeoLink complex ontology alignment benchmark, prompting an LLM with full ontologies (40-156 classes each) produced results that were “essentially unusable.” But a two-stage approach (first identifying which named modules were relevant, then prompting with just those modules, achieved 95% accuracy on the same benchmark (104 of 109 target mappings correct).

The improvement didn’t come from reducing size alone. It came from presenting information that cohered conceptually, matching how domain experts actually think about the problem space.

Related: 06-atom—conceptual-module, 05-molecule—two-stage-modular-prompting, 05-atom—context-window-limitations