Large language models (LLMs) exhibit impressive capabilities in natural language processing but are prone to generating hallucinations—false or misleading information presented as factual. This chapter systematically investigates the causes of hallucinations, their types, and the role of information retrieval in mitigating them. We present case studies, including a real-world flight investigation, to illustrate hallucination detection challenges. To address these issues, we propose an Adversarial Abductive Dialogue Framework with Reinforcement, which integrates multi-agent interactions, reinforcement learning, and logical abduction to detect and analyze hallucinations. This framework enables an explainable approach to hallucination detection, distinguishing between false positives and false negatives through adversarial dialogue. Furthermore, we explore strategies for correcting hallucinations, emphasizing the identification of reliable corrections and analyzing cases where reinforcement learning may introduce errors. Our evaluation compares the proposed framework with existing hallucination detection methods, focusing on entity- and attribute-level hallucinations. We provide insights into the effectiveness of our approach using various evaluation metrics. Finally, we discuss related work, conclusions, and future directions for improving explainable AI in hallucination mitigation. An appendix includes practical implementations of reliable correction mechanisms. This research contributes to the development of more trustworthy AI systems by enhancing the interpretability and reliability of hallucination detection and correction techniques.