  Detecting hallucinations in large language model (LLM) outputs is pivotal,
yet traditional fine-tuning for this classification task is impeded by the
expensive and quickly outdated annotation process, especially across numerous
vertical domains and in the face of rapid LLM advancements. In this study, we
introduce an approach that automatically generates both faithful and
hallucinated outputs by rewriting system responses. Experimental findings
demonstrate that a T5-base model, fine-tuned on our generated dataset,
surpasses state-of-the-art zero-shot detectors and existing synthetic
generation methods in both accuracy and latency, indicating efficacy of our
approach.
