Tackling LLM Hallucination with Abductive Reasoning

Large Language Models (LLMs) excel at generating fluent explanations but remain vulnerable to hallucinations—answers that are coherent yet unsupported. This paper proposes a unified theoretical and computational account of hallucination as a failure of abductive reasoning, where missing premises, weak confirmation, or counter-abductive defeat undermine the validity of an LLM’s conclusions. Drawing on Peirce’s triadic framework of deduction, induction, and abduction, we show how abductive inference serves as a missing-premise engine, a global coherence enforcer, and a minimality-based regularizer for Chain-of-Thought (CoT) reasoning. We formalize abductive logic programming (ALP) as the structural backbone for detecting entailment hallucinations and introduce a stepwise abductive analysis pipeline that identifies surprising observations, generates candidate explanations, evaluates them against evidence, and resolves them through defeasibility. Building on this foundation, we develop a theory of counter-abduction—an adversarial mechanism that generates rival hypotheses capable of defeating unsupported CoT reasoning. We further introduce Discourse-weighted ALP (D-ALP), which incorporates nucleus–satellite discourse structure to weight abductive hypotheses and improve both interpretability and robustness. Empirical evaluation across multiple reasoning benchmarks demonstrates that abductive and counter-abductive operators substantially reduce hallucinations, improve coherence, and enhance explanation quality. Ablation studies confirm the complementary roles of discourse structure, abductive minimality, and counter-abductive defeat. Taken together, our results position abduction—not merely as a philosophical concept, but as a practical, verifiable, and computationally grounded approach to improving LLM reasoning reliability.