  Large Language Models (LLMs) have transformed the Natural Language Processing
(NLP) landscape with their remarkable ability to understand and generate
human-like text. However, these models are prone to ``hallucinations'' --
outputs that do not align with factual reality or the input context. This paper
introduces the Hallucinations Leaderboard, an open initiative to quantitatively
measure and compare the tendency of each model to produce hallucinations. The
leaderboard uses a comprehensive set of benchmarks focusing on different
aspects of hallucinations, such as factuality and faithfulness, across various
tasks, including question-answering, summarisation, and reading comprehension.
Our analysis provides insights into the performance of different models,
guiding researchers and practitioners in choosing the most reliable models for
their applications.
