PhD: A ChatGPT-Prompted Visual hallucination Evaluation Dataset

TL;DR

This paper introduces PhD, a large-scale dataset created using ChatGPT to evaluate visual hallucinations in multimodal large language models across various tasks and contexts, revealing variability in hallucination susceptibility.

Contribution

The paper presents a novel, comprehensive dataset for objective visual hallucination evaluation of MLLMs, utilizing a ChatGPT-assisted pipeline to generate diverse, task-specific, and context-varied hallucination assessment data.

Findings

MLLMs show significant variability in hallucination across tasks and modes.

The dataset includes over 102k VQA triplets and 14k images.

PhD enables detailed analysis of hallucination patterns in MLLMs.

Abstract

Multimodal Large Language Models (MLLMs) hallucinate, resulting in an emerging topic of visual hallucination evaluation (VHE). This paper contributes a ChatGPT-Prompted visual hallucination evaluation Dataset (PhD) for objective VHE at a large scale. The essence of VHE is to ask an MLLM questions about specific images to assess its susceptibility to hallucination. Depending on what to ask (objects, attributes, sentiment, etc.) and how the questions are asked, we structure PhD along two dimensions, i.e. task and mode. Five visual recognition tasks, ranging from low-level (object / attribute recognition) to middle-level (sentiment / position recognition and counting), are considered. Besides a normal visual QA mode, which we term PhD-base, PhD also asks questions with specious context (PhD-sec) or with incorrect context ({PhD-icc), or with AI-generated counter common sense images (PhD-ccs). We construct PhD by a ChatGPT-assisted semi-automated pipeline, encompassing four pivotal modules: task-specific hallucinatory item (hitem) selection, hitem-embedded question generation, specious / incorrect context generation, and counter-common-sense (CCS) image generation. With over 14k daily images, 750 CCS images and 102k VQA triplets in total, PhD reveals considerable variability in MLLMs' performance across various modes and tasks, offering valuable insights into the nature of hallucination. As such, PhD stands as a potent tool not only for VHE but may also play a significant role in the refinement of MLLMs.