Despite making significant progress in multi-modal tasks, current Multi-modal
Large Language Models (MLLMs) encounter the significant challenge of
hallucination, which may lead to harmful consequences. Therefore, evaluating
MLLMs' hallucinations is becoming increasingly important in model improvement
and practical application deployment. Previous works are limited in high
evaluation costs (e.g., relying on humans or advanced LLMs) and insufficient
evaluation dimensions (e.g., types of hallucination and task). In this paper,
we propose an LLM-free multi-dimensional benchmark AMBER, which can be used to
evaluate both generative task and discriminative task including object
existence, object attribute and object relation hallucination. Based on AMBER,
we design a low-cost and efficient evaluation pipeline. Additionally, we
conduct a comprehensive evaluation and detailed analysis of mainstream MLLMs
including GPT-4V(ision), and also give guideline suggestions for mitigating
hallucinations. The data and code of AMBER are available at
https://github.com/junyangwang0410/AMBER.Comment: 11 pages, 4 figure