Convergence to Steady State in LLM-Generated Ontological Concepts
Abstract
Large Language Models (LLMs) offer a scalable method to generate candidate concepts for ontology expansion. However, the outputs depend on LLMs’ sampling parameters such as “temperature.” A high temperature leads to increased hallucinations. We consider two hypotheses: (1) LLM outputs for higher temperatures should be supersets of corresponding outputs for lower temperatures; (2) Repeated LLM outputs for lower temperatures should converge to a steady state faster than for higher temperatures. A steady state is defined as k consecutive iterations with no new concepts generated (k-convergence). In this study, we analyzed Environmental Determinants of Health (EnDOH) concepts retrieved at sampling temperatures ranging from 0.1 to 1.0 using concept-structured prompting. Contrary to expectations, higher temperatures do not produce strict supersets of lower temperature outputs. To assess convergence towards a steady state we iteratively executed fixed prompts at different temperatures. Our results confirm that lower temperatures indeed lead to faster convergence.
Cite this work
Naren Khatwani, Lijing Wang, Shmuel T. Klein, James Geller (2026). Convergence to Steady State in LLM-Generated Ontological Concepts. In *Studies in Health Technology and Informatics* (MIE 2026 - Medical Informatics Europe), 2026
