Neurosymbolic artificial intelligence via large language models and coherence-driven inference

• URL: http://arxiv.org/abs/2502.13953v1
• Date: Wed, 19 Feb 2025 18:53:16 GMT
• Title: Neurosymbolic artificial intelligence via large language models and coherence-driven inference
• Authors: Steve Huntsman, Jewell Thomas,
• Abstract summary: We generate sets of propositions that objectively instantiate graphs that support coherence-driven inference. We benchmark the ability of large language models to reconstruct coherence graphs from propositions expressed in natural language.
• Score: 3.522062800701924
• License:
• Abstract: We devise an algorithm to generate sets of propositions that objectively instantiate graphs that support coherence-driven inference. We then benchmark the ability of large language models (LLMs) to reconstruct coherence graphs from (a straightforward transformation of) propositions expressed in natural language, with promising results from a single prompt to models optimized for reasoning. Combining coherence-driven inference with consistency evaluations by neural models may advance the state of the art in machine cognition.

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