  Large language models are extensively applied across a wide range of tasks,
such as customer support, content creation, educational tutoring, and providing
financial guidance. However, a well-known drawback is their predisposition to
generate hallucinations. This damages the trustworthiness of the information
these models provide, impacting decision-making and user confidence. We propose
a method to detect hallucinations by looking at the structure of the latent
space and finding associations within hallucinated and non-hallucinated
generations. We create a graph structure that connects generations that lie
closely in the embedding space. Moreover, we employ a Graph Attention Network
which utilizes message passing to aggregate information from neighboring nodes
and assigns varying degrees of importance to each neighbor based on their
relevance. Our findings show that 1) there exists a structure in the latent
space that differentiates between hallucinated and non-hallucinated
generations, 2) Graph Attention Networks can learn this structure and
generalize it to unseen generations, and 3) the robustness of our method is
enhanced when incorporating contrastive learning. When evaluated against
evidence-based benchmarks, our model performs similarly without access to
search-based methods.
