The Instinctive Bias: Spurious Images lead to Illusion in MLLM s (2024)

Abstract: 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 visual illusion. To quantify the effect, we propose CorrelationQA, the first benchmark that assesses the visual illusion 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 code and datasets are available at https://github.com/MasaiahHan/CorrelationQA.

## introduction large language models (llms) have sparked a transformative shift in the field of artificial intelligence @xcite @xcite . following the development of llms, a series of multi-modal large language models (mllms) have emerged to enable llms with visual processing capabilities @xcite @xcite . typically, current mllms process visual inputs by converting them into visual tokens that share the same latent space as language tokens in llms. this conversion not * equal contribution. ## method in this section, we first present the background of multi-modal large language models (mllms) in commonsense question-answering (cqa) and the motivation of our study (2.1). next, we introduce the proposed automated pipeline to generate our correlationqa benchmark (2.2). finally, we provide the designed evaluation metrics to measure the sensitivity of mllms on spurious images (2.3). ## related work multi-modal large language models. benefiting from the exponential advancement of large language models (llms), a series of studies have introduced multi-modal large language models (mllms) by leveraging llms as their reasoning engine and textual interface @xcite @xcite . mllms achieve powerful visual understanding by training on image-text pairs. they can accurately extract semantic information from images and convert it into text that is easily comprehensible. additionally, they utilize llms' reasoning ability to complete multi-modal tasks such as visual question-answering (vqa) and captioning. ## conclusion in this paper, we demonstrate that current multimodal large language models (mllms) are easy to raise instinctive bias through deceptive images. we first design an automatic pipeline that utilizes gpt-4 and stable diffusion to generate image-text pairs with factual and spurious images. along with the designed pipeline, we construct a benchmark under 13 kinds of categories to evaluate the visual illusion of mllms under spurious visual inputs. furthermore, we present a comprehensive analysis of the sensitivity to instinctive bias in mllms across various categories and under different conditions. we hope our work aids in better assessing the comprehensive capabilities of mllms in real-world scenarios and understanding the modality alignment of mllms. through our findings, future work could concentrate on adjusting training strategies, aiding mllms in appropriately calibrating their attention to image information based on its relevance in suitable contexts.