# HaloQuest: A Visual Hallucination Dataset for Advancing Multimodal Reasoning

arXiv: https://arxiv.org/abs/2407.15680 · HTML: https://mlsanity.com/paper/2407.15680/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Placed in Hallucination detection and mitigation, one of 17 directions in LLM reasoning.
- Builds on 33 of its 65 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Placed in

- LLM reasoning / [Hallucination detection and mitigation](https://mlsanity.com/lr/hallucination-detection-and-mitigation/)
- Multimodal LLMs / vision-language models / [Hallucination mitigation in LVLMs](https://mlsanity.com/mllm/hallucination-mitigation-in-lvlms/)

## Builds on (most cited first, 30 of 33)

- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [Scaling Autoregressive Models for Content-Rich Text-to-Image Generation](https://mlsanity.com/paper/2206.10789/) (2022)
- [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (2023)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [Aligning Large Multimodal Models with Factually Augmented RLHF](https://mlsanity.com/paper/2309.14525/) (2023)
- [HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models](https://mlsanity.com/paper/2310.14566/) (2023)
- [OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models](https://mlsanity.com/paper/2308.01390/) (2023)
- [mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration](https://mlsanity.com/paper/2311.04257/) (2023)
- [Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks](https://mlsanity.com/paper/2208.10442/) (2022)
- [Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning](https://mlsanity.com/paper/2306.14565/) (2023)
- [DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models](https://mlsanity.com/paper/2210.14896/) (2022)
- [A Survey of Hallucination in Large Foundation Models](https://mlsanity.com/paper/2309.05922/) (2023)
- [A Survey on Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2402.00253/) (2024)
- [Analyzing and Mitigating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2310.00754/) (2023)
- [Detecting and Preventing Hallucinations in Large Vision Language Models](https://mlsanity.com/paper/2308.06394/) (2023)
- [Woodpecker: Hallucination Correction for Multimodal Large Language Models](https://mlsanity.com/paper/2310.16045/) (2023)
- [Med-HALT: Medical Domain Hallucination Test for Large Language Models](https://mlsanity.com/paper/2307.15343/) (2023)
- [Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges](https://mlsanity.com/paper/2311.03287/) (2023)
- [Evaluation and Analysis of Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2308.15126/) (2023)
- [Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision](https://mlsanity.com/paper/2311.07362/) (2023)
- [ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions](https://mlsanity.com/paper/2303.06594/) (2023)
- [VIGC: Visual Instruction Generation and Correction](https://mlsanity.com/paper/2308.12714/) (2023)
- [Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models](https://mlsanity.com/paper/2310.05338/) (2023)
- [Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training](https://mlsanity.com/paper/2210.07688/) (2022)
- [IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models](https://mlsanity.com/paper/2305.14985/) (2023)
- [How Easy is It to Fool Your Multimodal LLMs? An Empirical Analysis on Deceptive Prompts](https://mlsanity.com/paper/2402.13220/) (2024)

## Built on by (most cited first, 30 of 32)

- [Generate, but Verify: Reducing Hallucination in Vision-Language Models with Retrospective Resampling](https://mlsanity.com/paper/2504.13169/) (2025)
- [A Survey on Multimodal Benchmarks: In the Era of Large AI Models](https://mlsanity.com/paper/2409.18142/) (2024)
- [VidHal: Benchmarking Temporal Hallucinations in Vision LLMs](https://mlsanity.com/paper/2411.16771/) (2024)
- [M2-omni: Advancing Omni-MLLM for Comprehensive Modality Support with Competitive Performance](https://mlsanity.com/paper/2502.18778/) (2025)
- [DASH: Detection and Assessment of Systematic Hallucinations of VLMs](https://mlsanity.com/paper/2503.23573/) (2025)
- [HALP: Detecting Hallucinations in Vision-Language Models without Generating a Single Token](https://mlsanity.com/paper/2603.05465/) (2026)
- [Through the Magnifying Glass: Adaptive Perception Magnification for Hallucination-Free VLM Decoding](https://mlsanity.com/paper/2503.10183/) (2025)
- [LENS: Multi-level Evaluation of Multimodal Reasoning with Large Language Models](https://mlsanity.com/paper/2505.15616/) (2025)
- [JourneyBench: A Challenging One-Stop Vision-Language Understanding Benchmark of Generated Images](https://mlsanity.com/paper/2409.12953/) (2024)
- [PostAlign: Multimodal Grounding as a Corrective Lens for MLLMs](https://mlsanity.com/paper/2506.17901/) (2025)
- [Identify, Isolate, and Purge: Mitigating Hallucinations in LVLMs via Self-Evolving Distillation](https://mlsanity.com/paper/2507.04680/) (2025)
- [Measuring Epistemic Humility in Multimodal Large Language Models](https://mlsanity.com/paper/2509.09658/) (2025)
- [GHOST: Hallucination-Inducing Image Generation for Multimodal LLMs](https://mlsanity.com/paper/2509.25178/) (2025)
- [FINER: MLLMs Hallucinate under Fine-grained Negative Queries](https://mlsanity.com/paper/2603.17662/) (2026)
- [Can Humans Dream of Electric Sheep? Human-Written Samples for Fine-Grained Vision-and-Language Hallucination Benchmarking](https://mlsanity.com/paper/2608.01021/) (2026)
- [CAVE: Detecting and Explaining Commonsense Anomalies in Visual Environments](https://mlsanity.com/paper/2510.26006/) (2025)
- [CLASH: A Benchmark for Cross-Modal Contradiction Detection](https://mlsanity.com/paper/2511.19199/) (2025)
- [NEMO: Can Multimodal LLMs Identify Attribute-Modified Objects?](https://mlsanity.com/paper/2411.17794/) (2024)
- [Preemptive Hallucination Reduction: An Input-Level Approach for Multimodal Language Model](https://mlsanity.com/paper/2505.24007/) (2025)
- [Empowering Multimodal LLMs with External Tools: A Comprehensive Survey](https://mlsanity.com/paper/2508.10955/) (2025)
- [Draft and Refine with Visual Experts](https://mlsanity.com/paper/2511.11005/) (2025)
- [HalluWorld: A Controlled Benchmark for Hallucination via Reference World Models](https://mlsanity.com/paper/2605.19341/) (2026)
- [FaithSCAN: Model-Driven Single-Pass Hallucination Detection for Faithful Visual Question Answering](https://mlsanity.com/paper/2601.00269/) (2026)
- [Benchmarking Deflection and Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2604.12033/) (2026)
- [Verb Mirage: Unveiling and Assessing Verb Concept Hallucinations in Multimodal Large Language Models](https://mlsanity.com/paper/2412.04939/) (2024)
- [Transcoders Trace Visual Grounding and Hallucinations in Vision-Language Models](https://mlsanity.com/paper/2605.22902/) (2026)
- [CFCamo: A Counterfactual Detect-or-Abstain Framework for Camouflaged Object Detection](https://mlsanity.com/paper/2606.11231/) (2026)
- [MMOOC: A Comprehensive Benchmark for Out-of-Context Evaluation in Multimodal Large Language Models](https://mlsanity.com/paper/2607.27637/) (2026)
- [Overview of SHROOM-Visions 2026: A Shared Task on Hallucination Detection in Large Vision-Language Models](https://mlsanity.com/paper/2608.25662/) (2026)
- [Knowing What Not to Answer: Selective Non-Compliance in Vision-Language Models](https://mlsanity.com/paper/2609.04720/) (2026)

## Abstract (arXiv)

Hallucination has been a major problem for large language models and remains a critical challenge when it comes to multimodality in which vision-language models (VLMs) have to deal with not just textual but also visual inputs. Despite rapid progress in VLMs, resources for evaluating and addressing multimodal hallucination are limited and mostly focused on evaluation. This work introduces HaloQuest, a novel visual question answering dataset that captures various aspects of multimodal hallucination such as false premises, insufficient contexts, and visual challenges. A novel idea from HaloQuest is to leverage synthetic images, apart from real ones, to enable dataset creation at scale. With over 7.7K examples spanning across a wide variety of categories, HaloQuest was designed to be both a challenging benchmark for VLMs and a fine-tuning dataset for advancing multimodal reasoning. Our experiments reveal that current models struggle with HaloQuest, with all open-source VLMs achieving below 36% accuracy. On the other hand, fine-tuning on HaloQuest significantly reduces hallucination rates while preserving performance on standard reasoning tasks. Our results discover that benchmarking with generated images is highly correlated (r=0.97) with real images. Last but not least, we propose a novel Auto-Eval mechanism that is highly correlated with human raters (r=0.99) for evaluating VLMs. In sum, this work makes concrete strides towards understanding, evaluating, and mitigating hallucination in VLMs, serving as an important step towards more reliable multimodal AI systems in the future.