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Visual hallucination happens when an AI model describes or answers questions about an image in a way that does not match the visual evidence. It may claim an object is present when it is not, assign it the wrong attribute, or invent a relationship between things in the scene. “Hallucination” is a research term for this mismatch—not a claim that a model perceives or intends things as a person does.
What counts as a visual hallucination?
Researchers use the term for generated content that is inconsistent with an image. The 2024 National Science Review survey defines multimodal hallucination as responses from multimodal large language models (MLLMs) that are inconsistent with image content. A separate survey uses MLLM and large vision-language model (LVLM) for related systems that combine visual inputs with language generation; terminology varies across papers.
The mismatch can concern different parts of a description, and more than one kind of error can occur in the same answer:
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- Attribute: identifying an object but giving it an unsupported property, such as describing a dog as a different color.
- Relationship: naming the objects correctly but getting their relative positions or other relationships wrong.
A fluent, plausible sentence is not necessarily a grounded one: a scene detail can sound natural without being supported by the pixels.
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Why can an image model make things up?
There is no single cause established across all current image-language models. The survey by Zechen Bai and colleagues, submitted in 2024 and revised in 2025, organizes causes and mitigation across areas including data, model design, training, and inference. That breadth is a reason not to reduce the problem to one universal explanation, such as “bad training data” or a weak visual component.
Nor does an incorrect answer by itself show which part of a system failed. A model may produce a claim that conflicts with the image, but identifying why requires evidence about the model and the specific task. Evaluation results also depend on what a test asks and how it scores the response.
How researchers test visual hallucinations
Benchmarks measure different slices of the problem. A score from an open-ended caption task is not directly interchangeable with a score from yes-or-no object questions or a broader perception test.
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| Evaluation approach | What it tests | What the result represents |
|---|---|---|
| CHAIR | Open-ended image captions | Measures the share of sentences containing hallucinated objects or the share of mentioned objects that are hallucinated. |
| POPE | Repeated binary questions about whether a particular object appears in an image | Tests object-presence judgments in a constrained yes-or-no format. |
| MME | Broader perception categories, including object existence, count, position, and color | Covers several kinds of visual judgments rather than caption hallucination alone. |
| Model-based judging | Compares a response with references or image evidence | Its usefulness depends on what the evaluator can inspect. An image-aware judge can assess a response against the image; a text-only judge limited to captions or coordinates cannot directly inspect the visual input. |
These descriptions of CHAIR, POPE, MME, and judging approaches are summarized in the National Science Review survey. The prompt, annotations, response format, and evaluator all shape what a reported result means. A benchmark score is evidence about its task and setup, not a universal measure of how often every image model hallucinates.
Testing whether a model should abstain
Some visual question-answering tasks also include text retrieved from outside the image. That text can be noisy or misleading, or the available evidence may not support a confident answer. In those cases, reliability includes whether the model can decline to answer rather than confidently repeat an unsupported claim.
In a study published in July 2026, Nicholas Moratelli, Christopher Davis, Leonardo F. R. Ribeiro, Bill Byrne, and Gonzalo Iglesias introduced VLM-DeflectionBench, a benchmark with 2,775 samples across multimodal retrieval settings. Their abstract says: “Experiments across 20 state-of-the-art LVLMs indicate that models usually fail to deflect in the presence of noisy or misleading evidence.” The finding describes the systems and setup evaluated in that study; it is not a universal failure rate for all models or live products. See the paper in the Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics.
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What researchers are trying to do about it
Mitigation methods intervene at different points in a system’s workflow. The survey describes the following as research approaches, not guaranteed fixes:
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|---|---|---|
| Before generation | Use specialized or negative examples, or preference-based training, to encourage more faithful answers. | LRV-Instruction and LLaVA-RLHF |
| During generation | Change architecture, representations, or inference controls to influence how the model responds. | HallE-Switch, described as controlling the degree of “imagination” during inference |
| After generation | Inspect a response and revise it using additional image context. | Woodpecker, described as a training-free framework that uses expert models to add image context and correct an answer step by step |
These examples and their intervention categories are from the National Science Review survey. No one approach is established there as eliminating hallucinations or working best for every deployment. Abstention is another capability to assess, especially when answers depend on retrieved material that may conflict with the image.
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How to interpret claims about an image model
When reading a benchmark result or evaluating a model for a task, ask what evidence the result actually covers:
- What kind of error is counted? Object presence, attributes, relationships, counts, positions, colors, and conflicts with retrieved text are distinct targets.
- What answer format is tested? Writing a free-form caption and choosing yes or no test different behaviors.
- Can the evaluator see the image? A text-only evaluator may judge wording against a reference, but it cannot directly compare the answer with pixels it never receives.
- What systems and conditions were tested? A result belongs to the named task and evaluated model set; it does not establish how every current product behaves.
- Can the model decline when evidence is insufficient? In retrieval-based tasks, assess whether it handles misleading or conflicting information as well as whether it gives an answer.
The central distinction is between a claim that sounds plausible and one supported by the visual evidence. A benchmark can help measure particular kinds of mismatch, but its score should be read in light of its task, evaluator, and scope.
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