# Evaluating Image Hallucination in Text-to-Image Generation with Question-Answering

arXiv: https://arxiv.org/abs/2409.12784 · HTML: https://mlsanity.com/paper/2409.12784/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 19 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering](https://mlsanity.com/paper/2303.11897/) (2023)
- [DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models](https://mlsanity.com/paper/2202.04053/) (2022)
- [Re-Imagen: Retrieval-Augmented Text-to-Image Generator](https://mlsanity.com/paper/2209.14491/) (2022)
- [mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections](https://mlsanity.com/paper/2205.12005/) (2022)
- [GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual Generation](https://mlsanity.com/paper/2406.13743/) (2024)
- [What You See is What You Read? Improving Text-Image Alignment Evaluation](https://mlsanity.com/paper/2305.10400/) (2023)
- [ROME: Evaluating Pre-trained Vision-Language Models on Reasoning beyond Visual Common Sense](https://mlsanity.com/paper/2310.19301/) (2023)

## Built on by (most cited first, 16 of 16)

- [A Survey of Multimodal Hallucination Evaluation and Detection](https://mlsanity.com/paper/2507.19024/) (2025)
- [Hallucination Benchmark for Speech Foundation Models](https://mlsanity.com/paper/2510.16567/) (2025)
- [Hallucination Score: Towards Mitigating Hallucinations in Generative Image Super-Resolution](https://mlsanity.com/paper/2507.14367/) (2025)
- [Open Multimodal Retrieval-Augmented Factual Image Generation](https://mlsanity.com/paper/2510.22521/) (2025)
- [Mitigating Diffusion Model Hallucinations with Dynamic Guidance](https://mlsanity.com/paper/2510.05356/) (2025)
- [Review of Hallucination Understanding in Large Language and Vision Models](https://mlsanity.com/paper/2510.00034/) (2025)
- [On the Collapse Errors Induced by the Deterministic Sampler for Diffusion Models](https://mlsanity.com/paper/2508.16154/) (2025)
- [FAGER: Factually Grounded Evaluation and Refinement of Text-to-Image Models](https://mlsanity.com/paper/2605.19111/) (2026)
- [Debiasing Text-to-Image Evaluation via Implicit Cultural Alignment Reward Modeling](https://mlsanity.com/paper/2607.15740/) (2026)
- [Evaluating and Preserving High-level Fidelity in Super-Resolution](https://mlsanity.com/paper/2512.07037/) (2025)
- [Eliminating Hallucination in Diffusion-Augmented Interactive Text-to-Image Retrieval](https://mlsanity.com/paper/2601.20391/) (2026)
- [VIGIL: Tackling Hallucination Detection in Image Recontextualization](https://mlsanity.com/paper/2602.14633/) (2026)
- [ADaFuSE: Adaptive Diffusion-generated Image and Text Fusion for Interactive Text-to-Image Retrieval](https://mlsanity.com/paper/2603.21886/) (2026)
- [TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Design](https://mlsanity.com/paper/2605.20731/) (2026)
- [Presence Is Not Faithfulness: Figurative Vehicle Intrusion in Text-to-Image Generation](https://mlsanity.com/paper/2609.32188/) (2026)
- [EviScene: Beyond "Is It AI?" for Agentic Image Factuality Assessment](https://mlsanity.com/paper/2610.14609/) (2026)

## Abstract (arXiv)

Despite the impressive success of text-to-image (TTI) generation models, existing studies overlook the issue of whether these models accurately convey factual information. In this paper, we focus on the problem of image hallucination, where images created by generation models fail to faithfully depict factual content. To address this, we introduce I-HallA (Image Hallucination evaluation with Question Answering), a novel automated evaluation metric that measures the factuality of generated images through visual question answering (VQA). We also introduce I-HallA v1.0, a curated benchmark dataset for this purpose. As part of this process, we develop a pipeline that generates high-quality question-answer pairs using multiple GPT-4 Omni-based agents, with human judgments to ensure accuracy. Our evaluation protocols measure image hallucination by testing if images from existing TTI models can correctly respond to these questions. The I-HallA v1.0 dataset comprises 1.2K diverse image-text pairs across nine categories with 1,000 rigorously curated questions covering various compositional challenges. We evaluate five TTI models using I-HallA and reveal that these state-of-the-art models often fail to accurately convey factual information. Moreover, we validate the reliability of our metric by demonstrating a strong Spearman correlation (ρ=0.95) with human judgments. We believe our benchmark dataset and metric can serve as a foundation for developing factually accurate TTI generation models. Additional resources can be found on our project page: https://sgt-lim.github.io/I-HallA/.