The DOI matches its Crossref record; 58 of the 65 references it cites were found in a registry or an index.
Hallusionbench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models
Open accessAbstract
We introduce “HALLUSIONBENCH11“Hallusion” is a portmanteau of “hallucination” and “illusion.”,” a comprehensive benchmark designed for the evaluation of image-context rea-soning. This benchmark presents significant challenges to advanced large visual-language models (LVLMs), such as GPT-4V(ision), Gemini Pro Vision, Claude 3, and LLaVA-1.5, by emphasizing nuanced understanding and interpre-tation of visual data. The benchmark comprises 346 images paired with 1129 questions, all meticulously crafted by human experts. We introduce a novel structure for these visual questions designed to establish control groups. This structure enables us to conduct a quantitative analysis of the models' response tendencies, logical consistency, and various failure modes. In our evaluation on Hallusion-bench, we benchmarked 15 different models, highlighting a 31.42% question-pair accuracy achieved by the state-of-the-art GPT-4V. Notably, all other evaluated models achieve accuracy below 16%. Moreover, our analysis not only high-lights the observed failure modes, including language hal-lucination and visual illusion but also deepens an under-standing of these pitfalls. Our comprehensive case studies within Hallusionbench shed light on the challenges of hallucination and illusion in LVLMs. Based on these in-sights, we suggest potential pathways for their future im-provement. The benchmark and codebase can be accessed at https://github.com/tianyi-labIHallusionBench.
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“One such shortcoming is that models may not perceive the image in detail, often missing fine-grained details or ignoring the image entirely (Chen et al., 2024b; Guan et al., 2024; Tong et al., 2024).”
From Does Spatial Cognition Emerge in Frontier Models? (2024) · cites this paper as Guan et al., 2024
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“Recent studies have revealed that current LVLMs are suffering from multi-modal hallucinations, where models provide responses misaligned with the corresponding visual information [104, 187, 297].”
From A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions (2025) · cites this paper as [104]
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Cites
LLaMA: Open and Efficient Foundation Language Models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.13971 · Cited by 4,602
VQA: Visual Question Answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.279 · Cited by 4,487
Liu, H., Li, C., Wu, Q., & Lee, Y. J. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-1516 · Cited by 2,428
Proceedings of IEEE International Conference on Computer Vision
(1995) · doi:10.1109/iccv.1995 · Cited by 2,339
A Survey of Large Language Models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Dong, Z., Hou, Y., Zhang, B., Min, Y., Zhang, J., Liu, P., et al. (2026) · Frontiers of Computer Science · doi:10.1007/s11704-026-60308-3 · Cited by 2,093 · cited 1×
“In recent years, Large Language Models (LLMs) [9, 10, 26, 40, 45, 46, 61] have revolutionized the field of machine learning with the ability of language understanding and content generation, offering unprecedented ca-* pabilities and potentials across a multitude of applications.”
BackgroundSelf-citationA Survey of Large Language Models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.18223 · Cited by 2,090
Self-citationEvaluating Object Hallucination in Large Vision-Language Models
Li, Y., Du, Y., Zhou, K., Wang, J., Xin, Z., & Wen, J.-R. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.20 · Cited by 1,862
Improved Baselines with Visual Instruction Tuning
Liu, H., Li, C., Li, Y., & Lee, Y. J. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02484 · Cited by 1,748
Flamingo: a Visual Language Model for Few-Shot Learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.14198 · Cited by 1,555
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.12712 · Cited by 1,526
Flagged first, then by citations
7 of the 65 references couldn’t be matched to a known paper.
Research works printed but not linked: 12
- 2023, Gpt-4v(ision) system card.
- Antol, 2015, V qa: Visual question answering, 2425
- Awadalla, 2023, Openflamingo
- Bai, 2023, Qwen-vl: A frontier large vision-language model with versatile abilities, ArXiv, abs/2308.12966 · arXiv
- Chiang, 2023, Vicuna: An open-source chatbot impressing gpt-4 with 90% * chatgpt quality
- Driess, 2023, Palm-e: An embodied multimodallanguage model
- Fu, 2023, Mme: A compre-hensive evaluation benchmark for multimodallarge language models, arXiv preprint
- Li, 2023, To-wards understanding in-context learning with contrastive demonstrations and saliency maps, arXiv preprint
- Liu, 2023, Aligning large multi-modal model with robust instruction tuning, arXiv preprint
- Yang, 2023, Embodied multi-modal agent trained by an 11m from a parallel textworld
- Yin, 2023, A survey on multimodallarge language models, arXiv preprint
- Zhai, 2023, Investigating the catastrophic for-getting in multimodallarge language models, arXiv preprint
Cited by
Sorted by citations
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Self-citation
Metadata
- Authors
- Guan, T., Liu, F., Wu, X., Xian, R., Li, Z., Liu, X., Wang, X., Chen, L., Huang, F., Yacoob, Y., Manocha, D., & Zhou, T.
- Affiliations
- University of Maryland, College Park (US)
- ORCID
- Venue
- 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2024-06-16
- Pages
- 14375-14385
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4402726948
- Cited by
- 166
- References
- 65
- Citations by year
- 2026: 77 · 2025: 76 · 2024: 13
- FWCI
- 34.38
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- ARO (W911NF2310046,W911NF2310352)
- NSF (IIS-2147276 FAI)
- DOD (N00014-22-1-2335,FA9550-23-1-0048)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- hallucinations, optical illusions, large vision-language models, benchmark, failure modes, GPT-4V, LLaVA-1.5, control groups, visual question answering
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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