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.
@article{arxiv.2404.05904,
title = {The Hallucinations Leaderboard -- An Open Effort to Measure Hallucinations in Large Language Models},
author = {Giwon Hong and Aryo Pradipta Gema and Rohit Saxena and Xiaotang Du and Ping Nie and Yu Zhao and Laura Perez-Beltrachini and Max Ryabinin and Xuanli He and Clémentine Fourrier and Pasquale Minervini},
journal= {arXiv preprint arXiv:2404.05904},
year = {2024}
}