Abstract. Detecting hallucinations in large language models
(LLMs) is critical for their safety in many applications. Without
proper detection, these systems often provide harmful, unreliable
answers. In recent years, LLMs have been actively used in retrievalaugmented generation (RAG) settings. However, hallucinations remain even in this setting, and while numerous hallucination detection methods have been proposed, most approaches are not specifically designed for RAG systems. To overcome this limitation, we
introduce a hallucination detection method based on estimating the
distances between the distributions of prompt token embeddings
and language model response token embeddings1
. The method examines the geometric structure of token hidden states to reliably
extract a signal of factuality in text, while remaining friendly to
long sequences. Extensive experiments demonstrate that our method
achieves state-of-the-art or competitive performance. It also has
transferability from solving the NLI task to the hallucination detection task, making it a fully unsupervised and efficient method
with a competitive performance on the final task.