Hallucination of Multimodal Large Language Models: A Survey

TL;DR

This survey comprehensively reviews hallucination issues in multimodal large language models, analyzing causes, evaluation methods, mitigation strategies, and outlining future research directions to improve their reliability.

Contribution

It provides a detailed classification of hallucination causes, evaluation benchmarks, and mitigation techniques, offering a valuable resource for advancing MLLM robustness.

Findings

Identification of key causes of hallucination in MLLMs

Evaluation benchmarks and metrics for hallucination detection

Overview of mitigation strategies and their effectiveness

Abstract

This survey presents a comprehensive analysis of the phenomenon of hallucination in multimodal large language models (MLLMs), also known as Large Vision-Language Models (LVLMs), which have demonstrated significant advancements and remarkable abilities in multimodal tasks. Despite these promising developments, MLLMs often generate outputs that are inconsistent with the visual content, a challenge known as hallucination, which poses substantial obstacles to their practical deployment and raises concerns regarding their reliability in real-world applications. This problem has attracted increasing attention, prompting efforts to detect and mitigate such inaccuracies. We review recent advances in identifying, evaluating, and mitigating these hallucinations, offering a detailed overview of the underlying causes, evaluation benchmarks, metrics, and strategies developed to address this issue. Additionally, we analyze the current challenges and limitations, formulating open questions that delineate potential pathways for future research. By drawing the granular classification and landscapes of hallucination causes, evaluation benchmarks, and mitigation methods, this survey aims to deepen the understanding of hallucinations in MLLMs and inspire further advancements in the field. Through our thorough and in-depth review, we contribute to the ongoing dialogue on enhancing the robustness and reliability of MLLMs, providing valuable insights and resources for researchers and practitioners alike. Resources are available at: https://github.com/showlab/Awesome-MLLM-Hallucination.