Natural Language Prompts Outperform Structured Prompts

When using LLMs for technical knowledge tasks like ontology alignment validation, prompts written in conversational natural language consistently outperform prompts using structured, technical formatting.

In testing across nine ontology matching tasks, “natural language-friendly” prompts (e.g., “We have two entities… Do they mean the same thing?”) yielded more reliable binary classifications than structured prompts (e.g., “Source entity: X / Direct ontological parent: Y / Are these entities ontologically equivalent?”).

The hypothesis: LLMs are trained primarily on human-generated conversational text. Framing technical questions the way humans naturally ask them aligns better with the model’s learned patterns than imposing artificial structure.

Related: 06-molecule—prompt-design-dimensions-ontology, 05-atom—llm-oracle-20-percent-error-rate