Position: Logical Soundness is not a Reliable Criterion for Neurosymbolic Fact-Checking with LLMs

ArXi:2604.04177v1 Announce Type: new As large language models (LLMs) are increasing integrated into fact-checking pipelines, formal logic is often proposed as a rigorous means by which to mitigate bias, errors and hallucinations in these models' outputs. For example, some neurosymbolic systems verify claims by using LLMs to translate natural language into logical formulae and then checking whether the proposed claims are logically sound, i.e. whether they can be validly derived from premises that are verified to be true.