  Language models, particularly generative models, are susceptible to
hallucinations, generating outputs that contradict factual knowledge or the
source text. This study explores methods for detecting hallucinations in three
SemEval-2024 Task 6 tasks: Machine Translation, Definition Modeling, and
Paraphrase Generation. We evaluate two methods: semantic similarity between the
generated text and factual references, and an ensemble of language models that
judge each other's outputs. Our results show that semantic similarity achieves
moderate accuracy and correlation scores in trial data, while the ensemble
method offers insights into the complexities of hallucination detection but
falls short of expectations. This work highlights the challenges of
hallucination detection and underscores the need for further research in this
critical area.
