multimodal benchmark
POPE
Polling-based Object Probing Evaluation (POPE) is a benchmark for evaluating object hallucination in Large Vision-Language Models (LVLMs). POPE addresses the problem where LVLMs generate objects inconsistent with target images by using a polling-based query method that asks yes/no questions about object presence in images, providing more stable and flexible evaluation of object hallucination.
Updated Aug 11, 2026
Models2
Model coverage2
MetricScore
EvidenceB
POPE Ranking
Higher score ranks better on this benchmark.
| 01 | MI | 86.1% | 100.0% | 2 | C | |
| 02 | MI | 85.6% | 0.0% | 2 | C |
POPE Score Distribution
A closer view of the leading scores on this benchmark.
POPE Highlights
The leading models and scores on this benchmark.
What is POPE?
What POPE measures and how its scores work.
Polling-based Object Probing Evaluation (POPE) is a benchmark for evaluating object hallucination in Large Vision-Language Models (LVLMs). POPE addresses the problem where LVLMs generate objects inconsistent with target images by using a polling-based query method that asks yes/no questions about object presence in images, providing more stable and flexible evaluation of object hallucination.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- POPE
- Modality
- multimodal
- Primary category
- multimodal
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- pope|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about POPE.
Which model scores highest on POPE?
Phi-3.5-vision-instruct is currently ranked first with 86.1%.
What does POPE measure?
Polling-based Object Probing Evaluation (POPE) is a benchmark for evaluating object hallucination in Large Vision-Language Models (LVLMs). POPE addresses the problem where LVLMs generate objects inconsistent with target images by using a polling-based query method that asks yes/no questions about object presence in images, providing more stable and flexible evaluation of object hallucination.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
2 model results are currently shown.
Does this benchmark affect the overall score?
No. This benchmark is shown for reference but does not contribute to the overall score.