Large language models (LLMs) have recently experienced remarkable progress, where the advent of multi-modal large language models (MLLMs) has endowed LLMs with visual capabilities, leading to impressive performances in various multi-modal tasks. However, those powerful MLLMs such as GPT-4V still fail spectacularly when presented with certain image and text inputs. In this paper, we identify a typical class of inputs that baffles MLLMs, which consist of images that are highly relevant but inconsistent with answers, causing MLLMs to suffer from hallucination. To quantify the effect, we propose CorrelationQA, the first benchmark that assesses the hallucination level given spurious images. This benchmark contains 7,308 text-image pairs across 13 categories. Based on the proposed CorrelationQA, we conduct a thorough analysis on 9 mainstream MLLMs, illustrating that they universally suffer from this instinctive bias to varying degrees. We hope that our curated benchmark and evaluation results aid in better assessments of the MLLMs' robustness in the presence of misleading images. The resource is available in https://github.com/MasaiahHan/CorrelationQA.

翻译：大语言模型（LLMs）近期取得了显著进展，多模态大语言模型（MLLMs）的出现赋予了LLMs视觉能力，使其在各种多模态任务中展现出卓越性能。然而，诸如GPT-4V等强大的MLLMs在面对特定图像和文本输入时仍会出现严重失误。本文识别出一类典型的使MLLMs困惑的输入，这类输入包含与答案高度相关但不一致的图像，导致MLLMs产生幻觉。为量化这一影响，我们提出了CorrelationQA——首个评估虚假图像下幻觉水平的基准测试集。该基准包含7,308个图文对，涵盖13个类别。基于所提出的CorrelationQA，我们对9个主流MLLMs进行了深入分析，表明它们普遍不同程度地受到这种本能偏见的影响。我们期望我们构建的基准测试集和评估结果有助于在存在误导性图像时更好地评估MLLMs的鲁棒性。相关资源见https://github.com/MasaiahHan/CorrelationQA。