From Argumentation to Labeled Logic Program for LLM Verification - Galitsky, Boris (Proceedings of the AAAI Symposium Series, 2026)
Large language models (LLMs) often generate fluent butin-correct or unsupported statements, commonly referred toas hallucinations. We propose a hallucination detectionframe-work based on a Labeled Logic Program (LLP)architecture that integrates multiple reasoning paradigms,including logic programming, argumentation, probabilisticinference, and ab-ductive explanation. By enrichingsymbolic rules with seman-tic, epistemic, and contextuallabels and applying discourse-aware weighting, the systemprioritizes nucleus claims over peripheral statementsduring verification. Experiments on three benchmarkdatasets and a challenging clinical narrative dataset showthat LLP consistently outperforms classical symbolicvalidators, achieving the highest detection accuracy whencombined with discourse modeling. A human evaluationfurther demonstrates that logic-assisted explanationsim-prove both hallucination detection accuracy and usertrust. The results suggest that labeled symbolic reasoningwith dis-course awareness provides a robust andinterpretable ap-proach to LLM verification insafety-critical domains.