Neurosymbolic Learning for Inference-Time Argumentation

The paper introduces inference-time argumentation (ITA), a neurosymbolic framework designed for ternary claim verification that leverages formal argumentation semantics to enhance large language model (LLM) training and inference. ITA optimizes argument generation and scoring during training, resulting in predictions that are faithful to the underlying arguments rather than relying on post-hoc reasoning. Experimental results demonstrate that ITA outperforms argumentative baselines and competes effectively with direct-prediction methods on two datasets, providing deterministic and inspectable verdicts.