mlsanityData to 7 Oct 2026

H-POPE: Hierarchical Polling-based Probing Evaluation of Hallucinations in Large Vision-Language Models

arXiv 2411.04077 ↗First posted 6 Nov 2024cs.CVcited by 6

On the map: Multimodal LLMs / vision-language models → Hallucination mitigation in LVLMs

Abstract (arXiv)

By leveraging both texts and images, large vision language models (LVLMs) have shown significant progress in various multi-modal tasks. Nevertheless, these models often suffer from hallucinations, e.g., they exhibit inconsistencies between the visual input and the textual output. To address this, we propose H-POPE, a coarse-to-fine-grained benchmark that systematically assesses hallucination in object existence and attributes. Our evaluation shows that models are prone to hallucinations on object existence, and even more so on fine-grained attributes. We further investigate whether these models rely on visual input to formulate the output texts.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in Hallucination mitigation in LVLMs, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 7 of its 9 references that are in the map. First posted on arXiv 6 Nov 2024.

Builds on

7 of 9 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Evaluating Object Hallucination in Large Vision-Language Models17 May

Other references · 6

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2023Improved Baselines with Visual Instruction Tuningcited by 4,362
  3. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuningcited by 2,855
  4. 2018Object Hallucination in Image Captioningcited by 661
  5. 2021Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformerscited by 316
  6. 2024Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directionscited by 66

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025Evaluating Hallucination in Large Vision-Language Models based on Context-Aware Object Similaritiescited by 7
  2. 2026Seeing without Looking: Do Vision-Language Benchmarks Really Test Vision?cited by 6
  3. 2025HEDGE: Hallucination Estimation via Dense Geometric Entropy for VQA with Vision-Language Modelscited by 5
  4. 2026Can Humans Dream of Electric Sheep? Human-Written Samples for Fine-Grained Vision-and-Language Hallucination Benchmarkingcited by 4
  5. 2025What Makes "Good" Distractors for Object Hallucination Evaluation in Large Vision-Language Models?cited by 1
  6. 2026RCP: Representation Consistency Pruner for Mitigating Distribution Shift in Large Vision-Language Modelscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models
  2. 2025A Comprehensive Analysis for Visual Object Hallucination in Large Vision-Language Models
  3. 2024From Pixels to Tokens: Revisiting Object Hallucinations in Large Vision-Language Models
  4. 2024Measuring the Measurers: Quality Evaluation of Hallucination Benchmarks for Large Vision-Language Models
  5. 2025MIHBench: Benchmarking and Mitigating Multi-Image Hallucinations in Multimodal Large Language Models
  6. 2023Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
  7. 2024Reducing Hallucinations in Vision-Language Models via Latent Space Steering
  8. 2025Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations