We devise an algorithm to generate propositions that objectively instantiate graphs supporting coherence-driven inference. We also benchmark the ability of large language models (LLMs) to reconstruct coherence graphs from (a simple transformation of) propositions expressed in natural language, with promising results from a single prompt to reasoning-optimized LLMs. For example, o1/3/4-mini achieve perfect reconstruction half of the time on sparse graphs. Coherence-driven inference on consistency evaluations by LLMs may advance machine cognition capabilities.
@article{arxiv.2502.13953,
title = {Benchmarking graph construction by large language models for coherence-driven inference},
author = {Steve Huntsman and Jewell Thomas},
journal= {arXiv preprint arXiv:2502.13953},
year = {2025}
}