The Copy Ceiling: An Input-Exposure Control for Ontology-Grounded Generation over Curated Corpora

Research details a method to ground language models in curated ontology corpora, which significantly improved target-name recall from 0.265 to approximately 0.92 across ten models. However, this performance remains below a 'copy baseline' of 0.964, indicating that current evaluation metrics for recall may not adequately distinguish between reasoned answers and verbatim copies of input context. The study highlights limitations in assessing genuine reasoning in ontology-grounded generation.

This research provides critical insights into the evaluation of artificial intelligence models, particularly in natural language generation and knowledge grounding. It underscores the limitations of recall metrics in assessing true reasoning versus superficial reproduction of input, which is vital for developing robust and genuinely intelligent systems. Understanding these evaluation nuances is essential for setting realistic expectations and guiding future AI development.

A node was developed to ground a replaceable language model in a maintained ontology corpus.

Forward consideration, not a verified fact.