Benchmarking graph construction by large language models for coherence-driven inference

Steve Huntsman; Jewell Thomas

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arXiv · 2502.13953

Benchmarking graph construction by large language models for coherence-driven inference

Steve Huntsman·Jewell Thomas

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Abstract

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.

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Steve Huntsman, Jewell Thomas. 2025-02-19. Benchmarking graph construction by large language models for coherence-driven inference. https://arxiv.org/abs/2502.13953

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