Object Hallucination in Image Captioning
Open accessAbstract
Despite continuously improving performance, contemporary image captioning models are prone to "hallucinating" objects that are not actually in a scene.One problem is that standard metrics only measure similarity to ground truth captions and may not fully capture image relevance.In this work, we propose a new image relevance metric to evaluate current models with veridical visual labels and assess their rate of object hallucination.We analyze how captioning model architectures and learning objectives contribute to object hallucination, explore when hallucination is likely due to image misclassification or language priors, and assess how well current sentence metrics capture object hallucination.We investigate these questions on the standard image captioning benchmark, MSCOCO, using a diverse set of models.Our analysis yields several interesting findings, including that models which score best on standard sentence metrics do not always have lower hallucination and that models which hallucinate more tend to make errors driven by language priors. * Denotes equal contribution.NBT: A woman talking on a cell phone while sitting on a bench.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Deep learning based NLG generation is prone to hallucinate unintended text, which degrades system performance and fails to meet user expectations in many real-world scenarios.
“However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios.”
From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as [222]
The Introduction and §3.2 report that image-captioning models hallucinate objects, while §3.4 says these errors impair sentence quality. The Introduction also notes that incorrect captions are less preferable to some users. The paper does not establish the broader claim about deep-learning-based generation or user expectations across many real-world scenarios; page numbers were not supplied in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredLarge language model (LLM) systems often hallucinate false outputs and unsubstantiated answers.
“Large language model (LLM) systems, such as ChatGPT1 or Gemini2, can show impressive reasoning and question-answering capabilities but often ‘hallucinate’ false outputs and unsubstantiated answers3,4.”
From Detecting hallucinations in large language models using semantic entropy (2024) · cites this paper as [4]
The Introduction describes image-captioning models generating objects absent from an image, and Section 3.2 reports hallucinated objects in 7.4%–17.7% of tested captions. This supports the general point that generated outputs can be false, but the paper does not establish how often LLM systems such as ChatGPT or Gemini produce unsubstantiated answers. Page numbers were not available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThere is another contributor of hallucinations: training and modeling choices of neural models.
“This is because there is another contributor of hallucinations - training and modeling choices of neural models [121, 215, 222, 263].”
From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as [222]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · see citing works
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248 · see citing works
Long-term recurrent convolutional networks for visual recognition and description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298878 · Cited by 5,228 · see citing works
Self-citationVisual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107 · see citing works
CIDEr: Consensus-based image description evaluation
Vedantam, R., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299087 · Cited by 4,845 · see citing works
(2005) · Cited by 4,152 · see citing works
Self-Critical Sequence Training for Image Captioning
Rennie, S. J., Marcheret, E., Mroueh, Y., Ross, J., & Goel, V. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.131 · Cited by 2,177 · see citing works
SPICE: Semantic Propositional Image Caption Evaluation
Anderson, P., Fernando, B., Johnson, M., & Gould, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46454-1_24 · Cited by 1,986 · see citing works
Microsoft COCO Captions: Data Collection and Evaluation Server
Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollar, P., & Zitnick, C. L. (2015) · arXiv · doi:10.48550/arxiv.1504.00325 · Cited by 1,614 · see citing works
Lu, J., Yang, J., Batra, D., & Parikh, D. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00754 · Cited by 412 · see citing works
Rohrbach, A., Torabi, A., Rohrbach, M., Tandon, N., Pal, C., Larochelle, H., Courville, A. C., & Schiele, B. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0987-1 · Cited by 301 · see citing works
Self-citationDeep Compositional Captioning: Describing Novel Object Categories without Paired Training Data
Hendricks, L. A., Venugopalan, S., Rohrbach, M., Mooney, R. J., Saenko, K., & Darrell, T. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.8 · Cited by 280 · see citing works
Self-citationBottom-Up and Top-Down Attention for Image Captioning and VQA.Preprint
Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S. J., & Zhang, L. (2017) · arXiv (Cornell University) · Cited by 252 · see citing works
Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training
Shetty, R., Rohrbach, M., Hendricks, L. A., Fritz, M., & Schiele, B. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.445 · Cited by 231 · see citing works
Self-citationUnderstanding Blind People's Experiences with Computer-Generated Captions of Social Media Images
MacLeod, H., Bennett, C. L., Morris, M. R., & Cutrell, E. (2017) · Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025814 · Cited by 200 · see citing works
Discriminability Objective for Training Descriptive Captions
Luo, R., Shakhnarovich, G., Cohen, S. D., & Price, B. L. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00728 · Cited by 194 · see citing works
Learning to Evaluate Image Captioning
Cui, Y., Yang, G., Veit, A., Huang, X., & Belongie, S. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00608 · Cited by 140 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- Andrej Karpathy and Li Fei-Fei. 2015. Deep visual- semantic alignments for generating image descrip- tions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3128-3137.
- Mert Kilickaya, Aykut Erdem, Nazli Ikizler-Cinbis, and Erkut Erdem. 2016. Re-evaluating automatic metrics for image captioning. In European Chapter of the Association for Computational Linguistics.
- Anna Rohrbach, Makarand Tapaswi, Atousa Torabi, Tegan Maharaj, Marcus Rohrbach, Sanja Fi- dler Christopher Pal, and Bernt Schiele. 2017a. The Joint Video and Language Understand- ing Workshop: MovieQA and The Large Scale Movie Description Challenge (LSMDC). https://sites.google.com/site/ describingmovies/lsmdc-2017.
Cited by
Sorted by citations
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · see citing works
CLIPScore: A Reference-free Evaluation Metric for Image Captioning
Hessel, J., Holtzman, A., Forbes, M., Le Bras, R., & Choi, Y. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.595 · Cited by 1,232 · see citing works
Detecting hallucinations in large language models using semantic entropy
Farquhar, S., Kossen, J., Kuhn, L., & Gal, Y. (2024) · Nature · doi:10.1038/s41586-024-07421-0 · Cited by 934 · see citing works
A survey on multimodal large language models
Yin, S., Fu, C., Zhao, S., Li, K., Sun, X., Xu, T., & Chen, E. (2024) · National Science Review · doi:10.1093/nsr/nwae403 · Cited by 893 · see citing works
Evaluating 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 580 · see citing works
From Show to Tell: A Survey on Deep Learning-Based Image Captioning
Stefanini, M., Cornia, M., Baraldi, L., Cascianelli, S., Fiameni, G., & Cucchiara, R. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3148210 · Cited by 373 · see citing works
Wang, T., Huang, J., Zhang, H., & Sun, Q. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01077 · Cited by 291 · see citing works
RUBi: Reducing Unimodal Biases in Visual Question AnsweringPreprint
Cadène, R., Dancette, C., Ben-younes, H., Cord, M., & Parikh, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.10169 · Cited by 255 · see citing works
Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding
Leng, S., Zhang, H., Chen, G., Li, X., Lu, S., Miao, C., & Bing, L. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01316 · Cited by 199 · see citing works
Deep Learning Approaches on Image Captioning: A Review
Ghandi, T., Pourreza, H. R., & Mahyar, H. (2023) · ACM Computing Surveys · doi:10.1145/3617592 · Cited by 205 · see citing works
VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning
Chen, J., Guo, H., Yi, K., Li, B., & Elhoseiny, M. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01750 · Cited by 205 · see citing works
Evaluating the state-of-the-art of End-to-End Natural Language Generation: The E2E NLG challenge
Dušek, O., Novikova, J., & Rieser, V. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2019.06.009 · Cited by 194 · see citing works
Zhou, L., Kalantidis, Y., Chen, X., Corso, J. J., & Rohrbach, M. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00674 · Cited by 191 · see citing works
Connecting Vision and Language with Localized Narratives
Pont-Tuset, J., Uijlings, J., Changpinyo, S., Soricut, R., & Ferrari, V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58558-7_38 · Cited by 176 · see citing works
Causal Attention for Vision-Language Tasks
Yang, X., Zhang, H., Qi, G., & Cai, J. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00972 · Cited by 175 · see citing works
Human evaluation of automatically generated text: Current trends and best practice guidelines
van der Lee, C., Albert Gatt, A., van Miltenburg, E., & Krahmer, E. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101151 · Cited by 178 · see citing works
Human-Centered Artificial Intelligence for Designing Accessible Cultural Heritage
Pisoni, G., Díaz-Rodríguez, N., Gijlers, H., & Tonolli, L. (2021) · Applied Sciences · doi:10.3390/app11020870 · Cited by 183 · see citing works
Handling Divergent Reference Texts when Evaluating Table-to-Text Generation
Dhingra, B., Faruqui, M., Parikh, A., Chang, M.-W., Das, D., & Cohen, W. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1483 · Cited by 150 · see citing works
More Grounded Image Captioning by Distilling Image-Text Matching Model
Zhou, Y., Wang, M., Liu, D., Hu, Z., & Zhang, H. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00483 · Cited by 144 · see citing works
Reasoning with Language Model Prompting: A Survey
Qiao, S., Ou, Y., Zhang, N., Chen, X., Yao, Y., Deng, S., Tan, C., Huang, F., & Chen, H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.294 · Cited by 218 · see citing works
Huang, Q., Dong, X., Zhang, P., Wang, B., He, C., Wang, J., Lin, D., Zhang, W., & Yu, N. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01274 · Cited by 171 · see citing works
Comprehensive Image Captioning via Scene Graph Decomposition
Zhong, Y., Wang, L., Chen, J., Yu, D., & Li, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58568-6_13 · Cited by 137 · see citing works
Comprehending and Ordering Semantics for Image Captioning
Li, Y., Pan, Y., Yao, T., & Mei, T. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01746 · Cited by 138 · see citing works
Deconfounded Image Captioning: A Causal Retrospect
Yang, X., Zhang, H., & Cai, J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3121705 · Cited by 130 · see citing works
Invariant Grounding for Video Question Answering
Li, Y., Wang, X., Xiao, J., Ji, W., & Chua, T. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00294 · Cited by 123 · see citing works
Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering
Baek, J., Aji, A. F., & Saffari, A. (2023) · NLRSE · doi:10.18653/v1/2023.nlrse-1.7 · Cited by 122 · see citing works
Logical Natural Language Generation from Open-Domain Tables
Chen, W., Chen, J., Su, Y., Chen, Z., & Wang, W. Y. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.708 · Cited by 130 · see citing works
Yu, T., Yao, Y., Zhang, H., He, T., Han, Y., Cui, G., Hu, J., Liu, Z., Zheng, H.-T., & Sun, M. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01310 · Cited by 156 · see citing works
Performance of large language models on a neurology board-style examination
Schubert, M. C., Wick, W., & Venkataramani, V. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.46721 · Cited by 115 · see citing works
Trustworthy journalism through AI
Opdahl, A. L., Tessem, B., Dang‐Nguyen, D., Motta, E., Setty, V., Throndsen, E., Tverberg, A., & Trattner, C. (2023) · Data & Knowledge Engineering · doi:10.1016/j.datak.2023.102182 · Cited by 135 · see citing works
Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning
Wang, B. K., Li, G., Zhou, X., Chen, Z., Grossman, T., & Li, Y. (2021) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3472749.3474765 · Cited by 104 · see citing works
Learning to Collocate Neural Modules for Image Captioning
Yang, X., Zhang, H., & Cai, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00435 · Cited by 101 · see citing works
Ishmam, M. F., Shovon, M. S. H., Mridha, M. F., & Dey, N. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102270 · Cited by 100 · see citing works
Adversarial Inference for Multi-Sentence Video Description
Park, J. S., Rohrbach, M., Darrell, T., & Rohrbach, A. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00676 · Cited by 94 · see citing works
Self-citationNeural data-to-text generation: A comparison between pipeline and end-to-end architectures
Ferreira, T. C., van der Lee, C., van Miltenburg, E., & Krahmer, E. (2019) · Tilburg University Research Portal · Cited by 97 · see citing works
QuestEval: Summarization Asks for Fact-based Evaluation
Scialom, T., Dray, P.-A., Lamprier, S., Piwowarski, B., Staiano, J., Wang, A., & Gallinari, P. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.529 · Cited by 175 · see citing works
Semantic Noise Matters for Neural Natural Language Generation
Dušek, O., Howcroft, D. M., & Rieser, V. (2019) · Proceedings of the 12th International Conference on Natural Language Generation · doi:10.18653/v1/w19-8652 · Cited by 85 · see citing works
Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding
Dziri, N., Madotto, A., Zaïane, O., & Bose, A. J. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.168 · Cited by 81 · see citing works
Calibrated Language Models Must Hallucinate
Kalai, A. T., & Vempala, S. S. (2024) · Proceedings of the 56th Annual ACM Symposium on Theory of Computing · doi:10.1145/3618260.3649777 · Cited by 91 · see citing works
Controlled Hallucinations: Learning to Generate Faithfully from Noisy Data
Filippova, K. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.76 · Cited by 79 · see citing works
On hallucination and predictive uncertainty in conditional language generation
Xiao, Y., & Wang, W. Y. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.236 · Cited by 79 · see citing works
Are You Looking? Grounding to Multiple Modalities in Vision-and-Language Navigation
Hu, R., Fried, D., Rohrbach, A., Klein, D., Darrell, T., & Saenko, K. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1655 · Cited by 76 · see citing works
Self-citationLee, Y., Shin, T. H., Tessier, L., Javidan, A. P., Jung, J. J., Hong, D., Strong, A. T., McKechnie, T., Malone, S., Jin, D., et al. (2024) · Surgery for Obesity and Related Diseases · doi:10.1016/j.soard.2024.03.011 · Cited by 73 · see citing works
Multi-Modal Hallucination Control by Visual Information Grounding
Favero, A., Zancato, L., Trager, M., Choudhary, S., Perera, P., Achille, A., Swaminathan, A., & Soatto, S. (2024) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52733.2024.01356 · Cited by 299 · see citing works
Show, Deconfound and Tell: Image Captioning with Causal Inference
Liu, B., Wang, D., Yang, X., Zhou, Y. Z., Yao, R., Shao, Z., & Zhao, J. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01751 · Cited by 71 · see citing works
Li, Z., Wu, X., Du, H., Liu, F., Nghiem, H. T., & Shi, G. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00147 · Cited by 72 · see citing works
Park, J., Oh, C., & Kim, H. Y. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102705 · Cited by 71 · see citing works
TRUE: Re-evaluating Factual Consistency Evaluation
Honovich, O., Aharoni, R., Herzig, J., Taitelbaum, H., Kukliansy, D., Cohen, V., Scialom, T., Szpektor, I., Hassidim, A., & Matias, Y. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.287 · Cited by 75 · see citing works
Probing Image-Language Transformers for Verb Understanding
Hendricks, L. A., & Nematzadeh, A. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · doi:10.18653/v1/2021.findings-acl.318 · Cited by 63 · see citing works
Self-citationA Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation
Liu, T., Zhang, Y., Brockett, C., Mao, Y., Sui, Z., Chen, W., & Dolan, B. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.464 · Cited by 63 · see citing works
Iterative Edit-Based Unsupervised Sentence Simplification
Kumar, D., Mou, L., Golab, L., & Olga Vechtomova, A. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.707 · Cited by 62 · see citing works
Shifting the Baseline: Single Modality Performance on Visual Navigation & QA
Thomason, J., Gordon, D., & Bisk, Y. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1197 · Cited by 61 · see citing works
Accessible Cultural Heritage through Explainable Artificial Intelligence
Díaz-Rodríguez, N., & Pisoni, G. (2020) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3386392.3399276 · Cited by 55 · see citing works
TRUE: Re-evaluating Factual Consistency Evaluation
Honovich, O., Aharoni, R., Herzig, J., Taitelbaum, H., Kukliansy, D., Cohen, V., Scialom, T., Szpektor, I., Hassidim, A., & Matias, Y. (2022) · Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering · doi:10.18653/v1/2022.dialdoc-1.19 · Cited by 56 · see citing works
Knowing What to Learn: A Metric-Oriented Focal Mechanism for Image Captioning
Ji, J., Ma, Y., Sun, X., Zhou, Y., Wu, Y., & Ji, R. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3183434 · Cited by 50 · see citing works
Joint Commonsense and Relation Reasoning for Image and Video Captioning
Hou, J., Wu, X., Zhang, X., Qi, Y., Jia, Y., & Luo, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6731 · Cited by 50 · see citing works
HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data
Yu, Q., Li, J., Wei, L., Pang, L., Ye, W., Qin, B., Tang, S., Tian, Q., & Zhuang, Y. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01230 · Cited by 59 · see citing works
Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering
Baek, J., Aji, A., & Saffari, A. (2023) · Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023) · doi:10.18653/v1/2023.matching-1.7 · Cited by 51 · see citing works
“However, since the knowledge in LLMs might be incomplete, incorrect, and outdated, they often generate factually wrong answers, known as hallucination (Rohrbach et al., 2018) (See Figure 1a).”
BackgroundMitigating Gender Bias in Captioning Systems
Tang, R., Du, M., Li, Y., Liu, Z., Zou, N., & Hu, X. (2021) · Proceedings of the Web Conference 2021 · doi:10.1145/3442381.3449950 · Cited by 50 · see citing works
Visual Cluster Grounding for Image Captioning
Jiang, W., Zhu, M., Fang, Y., Shi, G., Zhao, X., & Liu, Y. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3177318 · Cited by 48 · see citing works
Efficient GPT-4V level multimodal large language model for deployment on edge devices
Yao, Y., Yu, T., Zhang, A., Wang, C., Cui, J., Zhu, H., Cai, T., Chen, C., Li, H., Zhao, W., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-61040-5 · Cited by 50 · see citing works
Cross-modal Coherence Modeling for Caption Generation
Alikhani, M., Sharma, P. K., Li, S., Soricut, R., & Stone, M. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.583 · Cited by 46 · see citing works
Consensus Graph Representation Learning for Better Grounded Image Captioning
Zhang, W., Shi, H., Tang, S., Xiao, J., Yu, Q., & Zhuang, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i4.16452 · Cited by 45 · see citing works
Positive-Augmented Contrastive Learning for Image and Video Captioning Evaluation
Sarto, S., Barraco, M., Cornia, M., Baraldi, L., & Cucchiara, R. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00668 · Cited by 48 · see citing works
Extractive Opinion Summarization in Quantized Transformer Spaces
Angelidis, S., Amplayo, R. K., Suhara, Y., Wang, X., & Lapata, M. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00366 · Cited by 85 · see citing works
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
Qi, Z., Dong, R., Zhang, S., Geng, H., Han, C., Ge, Z., Yi, L., & Ma, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72775-7_13 · Cited by 54 · see citing works
PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation
Hua, X., & Wang, L. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.57 · Cited by 45 · see citing works
Efficient Counterfactual Debiasing for Visual Question Answering
Kolling, C., Móre, M. D., Gavenski, N. S., Pooch, E., Parraga, O., & Barros, R. C. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00263 · Cited by 42 · see citing works
Exposing and Mitigating Spurious Correlations for Cross-Modal Retrieval
Kim, J. M., Koepke, A. S., Schmid, C., & Akata, Z. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00257 · Cited by 39 · see citing works
Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs
Liu, S., Zheng, K., & Chen, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73010-8_8 · Cited by 50 · see citing works
Self-Supervised and Controlled Multi-Document Opinion Summarization
Elsahar, H., Coavoux, M., Rozen, J., & Gallé, M. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.141 · Cited by 39 · see citing works
Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning
Biten, A. F., Gomez, L., & Karatzas, D. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00253 · Cited by 43 · see citing works
Auto-Parsing Network for Image Captioning and Visual Question Answering
Yang, X., Gao, C., Zhang, H., & Cai, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00220 · Cited by 38 · see citing works
Hallucination of Multimodal Large Language Models: A Survey
Bai, Z., Wang, P., Xiao, T., He, T., Han, Z., Zhang, Z., & Shou, M. Z. (2024) · arXiv · doi:10.48550/arxiv.2404.18930 · Cited by 38 · see citing works
“CHAIR [90] .”
InfluentialExplain and improve: LRP-inference fine-tuning for image captioning models
Sun, J., Lapuschkin, S., Samek, W., & Binder, A. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.07.008 · Cited by 47 · see citing works
Learning to Generate Grounded Visual Captions Without Localization Supervision
Ma, C., Kalantidis, Y., AlRegib, G., Vajda, P., Rohrbach, M., & Kira, Z. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58523-5_21 · Cited by 35 · see citing works
Proposed Framework for Human-like Language Processing of ChatGPT in Academic Writing
Mahyoob, M., Algaraady, J., & Alblwi, A. (2023) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v18i14.41725 · Cited by 42 · see citing works
Transparent Human Evaluation for Image Captioning
Kasai, J., Sakaguchi, K., Dunagan, L., Morrison, J., Bras, R. J. L., Choi, Y., & Smith, N. A. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.254 · Cited by 34 · see citing works
EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching
Shi, Y., Yang, X., Xu, H., Yuan, C., Li, B., Hu, W., & Zha, Z.-J. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01740 · Cited by 34 · see citing works
A Review of Transformer-Based Approaches for Image Captioning
Ondeng, O., Absaloms, H. O., & Akuon, P. O. (2023) · Applied Sciences · doi:10.3390/app131911103 · Cited by 33 · see citing works
Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training
Dai, W., Liu, Z., Ji, Z., Su, D., & Fung, P. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.156 · Cited by 40 · see citing works
CLAIR: Evaluating Image Captions with Large Language Models
Chan, D. M., Petryk, S., Gonzalez, J. E., Darrell, T., & Canny, J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.841 · Cited by 31 · see citing works
Self-citationVCoder: Versatile Vision Encoders for Multimodal Large Language Models
Jain, J., Yang, J., & Shi, H. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02644 · Cited by 35 · see citing works
With a Little Help from your own Past: Prototypical Memory Networks for Image Captioning
Barraco, M., Sarto, S., Cornia, M., Baraldi, L., & Cucchiara, R. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00282 · Cited by 30 · see citing works
Wang, L., He, J., Li, S., Liu, N., & Lim, E. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-53302-0_3 · Cited by 31 · see citing works
Towards Unique and Informative Captioning of Images
Wang, Z., Feng, B. T., Narasimhan, K., & Russakovsky, O. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58571-6_37 · Cited by 29 · see citing works
Hybrid attention network for image captioning
Jiang, W., Li, Q., Zhan, K., Fang, Y., & Shen, F. (2022) · Displays · doi:10.1016/j.displa.2022.102238 · Cited by 29 · see citing works
Benchmark Evaluations, Applications, and Challenges of Large Vision Language Models: A SurveyPreprint
Li, Z., Wu, X., Du, H., Nghiem, H. T., & Shi, G. (2025) · Qeios · doi:10.32388/gxr68q · Cited by 30 · see citing works
Relational Graph Learning for Grounded Video Description Generation
Zhang, W., Wang, X. E., Tang, S., Shi, H., Shi, H., Xiao, J., Zhuang, Y., & Wang, W. Y. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413746 · Cited by 28 · see citing works
CAST: Cross-Modal Retrieval and Visual Conditioning for image captioning
Cao, S., An, G., Cen, Y., Yang, Z., & Lin, W. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110555 · Cited by 28 · see citing works
Deconfounded Image Captioning: A Causal RetrospectPreprint
Yang, X., Zhang, H., & Cai, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.03923 · Cited by 38 · see citing works
AlAfnan, M. A. (2024) · Journal of Artificial Intelligence and Technology · doi:10.37965/jait.2024.0549 · Cited by 38 · see citing works
Large multimodal models evaluation: a survey
Zhang, Z.-C., Wang, J.-Y., Wen, F.-R., Guo, Y.-J., Zhao, X., Fang, X., Ding, S.-Y., Jia, Z.-H., Xiao, J.-H., Shen, Y., et al. (2025) · Science China Information Sciences · doi:10.1007/s11432-025-4676-4 · Cited by 64 · see citing works
QuestEval: Summarization Asks for Fact-based EvaluationPreprint
Scialom, T., Dray, P.-A., Gallinari, P., Lamprier, S., Piwowarski, B., Staiano, J., & Wang, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.12693 · Cited by 22 · see citing works
Evidence-based Factual Error Correction
Thorne, J., & Vlachos, A. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.256 · Cited by 39 · see citing works
Fusion Models for Improved Image Captioning
Kalimuthu, M., Mogadala, A., Mosbach, M., & Klakow, D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68780-9_32 · Cited by 21 · see citing works
LUNA: A Model-Based Universal Analysis Framework for Large Language Models
Song, D., Xie, X., Song, J., Zhu, D., Huang, Y., Juefei-Xu, F., & Ma, L. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3411928 · Cited by 25 · see citing works
Chen, Y., Sikka, K., Cogswell, M., Ji, H., & Divakaran, A. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01350 · Cited by 29 · see citing works
An, W., Tian, F., Leng, S., Nie, J., Lin, H., Wang, Q., Chen, P., Zhang, X., & Lu, S. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02784 · Cited by 32 · see citing works
Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision
Lee, S., Park, S. H., Jo, Y., & Seo, M. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2024.naacl-long.23 · Cited by 20 · see citing works
- Authors
- Rohrbach, A., Hendricks, L. A., Burns, K., Darrell, T., & Saenko, K.
- Affiliations
- Max Planck Society (DE)
- University of California, Berkeley (US)
- Boston University (US)
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Published
- 2018
- Pages
- 4035-4045
- OpenAlex
- W2892087592
- Cited by
- 420
- References
- 19
- Citations by year
- 2026: 103 · 2025: 80 · 2024: 70 · 2023: 42 · 2022: 32 · 2021: 45 · 2020: 32 · 2019: 15 · 2018: 1
- FWCI
- 7.01
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- object hallucination, image captioning, hallucination evaluation, language priors
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
