ITA is a neurosymbolic framework that optimizes LLM argument generation and scoring via argumentation semantics to yield faithful ternary claim verifications on two datasets.
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LLMs show structured attribute-driven decisions that a behavioral model can predict, but self-reports recover those drivers only partially, indicating superficial beliefs.