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        <title>Visual Hallucinations of Multi-modal Large Language Models</title>
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    <abstract>Visual hallucination (VH) means that a multi-modal LLM (MLLM) imagines incorrect details about an image in visual question answering. Existing studies find VH instances only in existing image datasets, which results in biased understanding of MLLMs’ performance under VH due to limited diversity of such VH instances. In this work, we propose a tool called VHTest to generate a diverse set of VH instances. Specifically, VHTest finds some initial VH instances in existing image datasets (e.g., COCO), generates a text description for each VH mode, and uses a text-to-image generative model (e.g., DALL-E-3) to generate VH images based on the text descriptions. We collect a benchmark dataset with 1,200 VH instances in 8 VH modes using VHTest. We find that existing MLLMs such as GPT-4, LLaVA-1.5, and MiniGPT-v2 hallucinate for a large fraction of the instances in our benchmark. Moreover, we find that fine-tuning an MLLM using our benchmark dataset reduces its likelihood to hallucinate without sacrificing its performance on other benchmarks. Our benchmarks are publicly available: https://github.com/wenhuang2000/VHTest.</abstract>
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%0 Conference Proceedings
%T Visual Hallucinations of Multi-modal Large Language Models
%A Huang, Wen
%A Liu, Hongbin
%A Guo, Minxin
%A Gong, Neil
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F huang-etal-2024-visual
%X Visual hallucination (VH) means that a multi-modal LLM (MLLM) imagines incorrect details about an image in visual question answering. Existing studies find VH instances only in existing image datasets, which results in biased understanding of MLLMs’ performance under VH due to limited diversity of such VH instances. In this work, we propose a tool called VHTest to generate a diverse set of VH instances. Specifically, VHTest finds some initial VH instances in existing image datasets (e.g., COCO), generates a text description for each VH mode, and uses a text-to-image generative model (e.g., DALL-E-3) to generate VH images based on the text descriptions. We collect a benchmark dataset with 1,200 VH instances in 8 VH modes using VHTest. We find that existing MLLMs such as GPT-4, LLaVA-1.5, and MiniGPT-v2 hallucinate for a large fraction of the instances in our benchmark. Moreover, we find that fine-tuning an MLLM using our benchmark dataset reduces its likelihood to hallucinate without sacrificing its performance on other benchmarks. Our benchmarks are publicly available: https://github.com/wenhuang2000/VHTest.
%U https://aclanthology.org/2024.findings-acl.573
%P 9614-9631

Markdown (Informal)

[Visual Hallucinations of Multi-modal Large Language Models](https://aclanthology.org/2024.findings-acl.573) (Huang et al., Findings 2024)

• Visual Hallucinations of Multi-modal Large Language Models (Huang et al., Findings 2024)

ACL

• Wen Huang, Hongbin Liu, Minxin Guo, and Neil Gong. 2024. Visual Hallucinations of Multi-modal Large Language Models. In Findings of the Association for Computational Linguistics ACL 2024, pages 9614–9631, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.