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[Submitted on 19 Feb 2026 (v1), last revised 31 Aug 2026 (this version, v2)]
Abstract:Language models exhibit fundamental limitations — hallucination, brittleness, and lack of formal grounding — that are particularly problematic in high-stakes specialist fields requiring verifiable reasoning. I investigate whether formal domain ontologies can enhance language model reliability through retrieval-augmented generation. Using mathematics as proof of concept, I implement a neuro-symbolic pipeline leveraging the OpenMath ontology with hybrid retrieval and cross-encoder reranking to inject relevant definitions into model prompts. Evaluation on the MATH benchmark with three open-source models reveals that ontology-guided context improves performance when retrieval quality is high, but irrelevant context actively degrades it — highlighting both the promise and challenges of neuro-symbolic approaches.
Submission history
From: Marcelo Labre [view email]
[v1]
Thu, 19 Feb 2026 20:45:16 UTC (2,970 KB)
[v2]
Mon, 31 Aug 2026 18:34:12 UTC (2,970 KB)
추출 본문 · 출처: arxiv.org · https://arxiv.org/abs/2602.17826