  In Natural Language Generation (NLG), contemporary Large Language Models
(LLMs) face several challenges, such as generating fluent yet inaccurate
outputs and reliance on fluency-centric metrics. This often leads to neural
networks exhibiting "hallucinations". The SHROOM challenge focuses on
automatically identifying these hallucinations in the generated text. To tackle
these issues, we introduce two key components, a data augmentation pipeline
incorporating LLM-assisted pseudo-labelling and sentence rephrasing, and a
voting ensemble from three models pre-trained on Natural Language Inference
(NLI) tasks and fine-tuned on diverse datasets.
