  Large Vision-Language Models (LVLMs) have recently achieved remarkable
success. However, LVLMs are still plagued by the hallucination problem, which
limits the practicality in many scenarios. Hallucination refers to the
information of LVLMs' responses that does not exist in the visual input, which
poses potential risks of substantial consequences. There has been limited work
studying hallucination evaluation in LVLMs. In this paper, we propose
Hallucination Evaluation based on Large Language Models (HaELM), an LLM-based
hallucination evaluation framework. HaELM achieves an approximate 95%
performance comparable to ChatGPT and has additional advantages including low
cost, reproducibility, privacy preservation and local deployment. Leveraging
the HaELM, we evaluate the hallucination in current LVLMs. Furthermore, we
analyze the factors contributing to hallucination in LVLMs and offer helpful
suggestions to mitigate the hallucination problem. Our training data and human
annotation hallucination data will be made public soon.

Machine Learning;Artificial Intelligence;Computation and Language;Computer Vision and Pattern Recognition