The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Promptify: Text-to-Image Generation through Interactive Prompt Exploration with Large Language Models
Overview
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
Verification
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?
Recent text-to-image generation systems that generate multiple images for exploration focus on helping users refine their prompts to find the image that fits their desired quality or characteristics.
“recent text-to-image generation systems that generate multiple images for exploration focus on helping users refine their prompts to find the image that fits their desired quality or characteristics [11, 26].”
From Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation (2024) · cites this paper as [11]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01042 · Cited by 20,302
Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293
Adding Conditional Control to Text-to-Image Diffusion Models
Zhang, L., Rao, A., & Agrawala, M. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00355 · Cited by 4,547
Li, J., Li, D., Xiong, C., & Hoi, S. (2022) · arXiv · doi:10.48550/arxiv.2201.12086 · Cited by 1,741
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2021) · arXiv · doi:10.48550/arxiv.2112.10752 · Cited by 737
Design Guidelines for Prompt Engineering Text-to-Image Generative Models
Liu, V., & Chilton, L. B. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501825 · Cited by 639 · cited 4×
“However, recent studies have revealed that crafting prompts–the primary means of steering image generation–can be a challenging task [10, 22, 26].”
BackgroundInfluentialDesign Guidelines for Prompt Engineering Text-to-Image Generative Models
Liu, V., & Chilton, L. B. (2021) · arXiv · doi:10.48550/arxiv.2109.06977 · Cited by 635
PaLM-E: An Embodied Multimodal Language Model
Driess, D., Xia, F., Sajjadi, M. S. M., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.03378 · Cited by 591
Flagged first, then by citations
Research works printed but not linked: 36
- Steven Bird, Ewan Klein, and Edward Loper. 2009. Natural language processing with Python: analyzing text with the natural language toolkit. " O’Reilly Media, Inc.".
- 10.1145/2501988.2502008 · DOI
- 10.1145/3526113.3545638 · DOI
- Noyan Evirgen and Xiang’Anthony’ Chen. 2023. GANravel: User-Driven Direction Disentanglement in Generative Adversarial Networks. arXiv preprint arXiv:2302.00079 (2023). · arXiv
- 10.1126/science.1136800 · DOI
- Sandra G Hart and LE Staveland. 1988. Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research.[W]: PA Hancock, N. Meshkati (Eds.): Human Mental Workload.
- Hyung-Kwon Ko, Gwanmo Park, Hyeon Jeon, Jaemin Jo, Juho Kim, and Jinwook Seo. 2022. Large-scale Text-to-Image Generation Models for Visual Artists’ Creative Works. ArXiv abs/2210.08477 (2022). · arXiv
- Jiachang Liu Dinghan Shen Yizhe Zhang Bill Dolan Lawrence Carin and Weizhu Chen. 2021. What Makes Good In-Context Examples for GPT-3?arxiv:2101.06804 [cs.CL] · arXiv
- 10.1145/3526113.3545621 · DOI · Paper page
- Vivian Liu Jo Vermeulen George Fitzmaurice and Justin Matejka. 2022. 3DALL-E: Integrating Text-to-Image AI in 3D Design Workflows. arxiv:2210.11603 [cs.HC] · arXiv
- Elman Mansimov Emilio Parisotto Jimmy Lei Ba and Ruslan Salakhutdinov. 2016. Generating Images from Captions with Attention. arxiv:1511.02793 [cs.LG] · arXiv
- 10.1145/258734.258887 · DOI
- 10.1145/3173574.3173943 · DOI · Paper page
- 10.1145/1622176.1622214 · DOI · Paper page
- Anh Nguyen Jeff Clune Yoshua Bengio Alexey Dosovitskiy and Jason Yosinski. 2017. Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space. arxiv:1612.00005 [cs.CV] · DOI
- OpenAI. 2023. GPT-4 Technical Report. arxiv:2303.08774 [cs.CL] · arXiv
- Jonas Oppenlaender. 2022. A Taxonomy of Prompt Modifiers for Text-To-Image Generation. arxiv:2204.13988 [cs.MM] · arXiv
- Aditya Ramesh Prafulla Dhariwal Alex Nichol Casey Chu and Mark Chen. 2022. Hierarchical Text-Conditional Image Generation with CLIP Latents. arxiv:2204.06125 [cs.CV] · arXiv
- Aditya Ramesh Mikhail Pavlov Gabriel Goh Scott Gray Chelsea Voss Alec Radford Mark Chen and Ilya Sutskever. 2021. Zero-Shot Text-to-Image Generation. arxiv:2102.12092 [cs.CV] · arXiv
- Scott Reed Zeynep Akata Santosh Mohan Samuel Tenka Bernt Schiele and Honglak Lee. 2016. Learning What and Where to Draw. arxiv:1610.02454 [cs.CV] · arXiv
- Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee. 2016. Generative Adversarial Text to Image Synthesis. In Proceedings of The 33rd International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 48), Maria Florina Balcan and Kilian Q. Weinberger (Eds.). PMLR, New York, New York, USA, 1060–1069. https://proceedings.mlr.press/v48/reed16.html
- 10.1145/2980179.2980242 · DOI
- Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep Unsupervised Learning using Nonequilibrium Thermodynamics. In Proceedings of the 32nd International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 37), Francis Bach and David Blei (Eds.). PMLR, Lille, France, 2256–2265. https://proceedings.mlr.press/v37/sohl-dickstein15.html
- John Swales. 2014. The concept of discourse community. Wardle and Downs (2014), 215–28.
- 10.1145/571985.571996 · DOI
- Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE.Journal of machine learning research 9, 11 (2008).
- Yunlong Wang, Shuyuan Shen, and Brian Y Lim. 2023. RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions. arXiv preprint arXiv:2302.09466 (2023). · arXiv
- Zijie J. Wang, Evan Montoya, David Munechika, Haoyang Yang, Benjamin Hoover, and Duen Horng Chau. 2022. DiffusionDB: A Large-Scale Prompt Gallery Dataset for Text-to-Image Generative Models. arXiv:2210.14896 [cs] (2022). https://arxiv.org/abs/2210.14896 · arXiv
- Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2023. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arxiv:2201.11903 [cs.CL] · arXiv
- Yuxin Wen Neel Jain John Kirchenbauer Micah Goldblum Jonas Geiping and Tom Goldstein. 2023. Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery. arxiv:2302.03668 [cs.LG] · arXiv
- Sam Witteveen and Martin Andrews. 2022. Investigating Prompt Engineering in Diffusion Models. arxiv:2211.15462 [cs.CV] · arXiv
- 10.1145/2807442.2807448 · DOI
- 10.1145/2766908 · DOI
- 10.1145/2702123.2702398 · DOI · Paper page
- Han Zhang Tao Xu Hongsheng Li Shaoting Zhang Xiaogang Wang Xiaolei Huang and Dimitris Metaxas. 2017. StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks. arxiv:1612.03242 [cs.CV] · DOI · Paper page
- Tony Z. Zhao Eric Wallace Shi Feng Dan Klein and Sameer Singh. 2021. Calibrate Before Use: Improving Few-Shot Performance of Language Models. arxiv:2102.09690 [cs.CL] · arXiv
Cited by
Sorted by citations
Suh, S., Chen, M., Min, B., Li, T. J.-J., & Xia, H. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642400 · Cited by 148
PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement
Wang, Z., Huang, Y., Song, D., Ma, L., & Zhang, T. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642803 · Cited by 115
"I look at it as the king of knowledge": How Blind People Use and Understand Generative AI Tools
Adnin, R., & Das, M. (2024) · The 26th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3663548.3675631 · Cited by 73
A Taxonomy for Human-LLM Interaction Modes: An Initial Exploration
Gao, J., Gebreegziabher, S. A., Choo, K. T. W., Li, T. J.-J., Perrault, S. T., & Malone, T. W. (2024) · Extended Abstracts of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613905.3650786 · Cited by 57
“Such UI enhancements allow users to refne their original interaction fows, leading to improved fnal or intermediate outputs [6, 43].”
AIdeation: Designing a Human-AI Collaborative Ideation System for Concept Designers
Wang, W.-F., Lu, C.-T., Ponsa i Campanyà, N., Chen, B.-Y., & Chen, M. Y. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714148 · Cited by 50
Nabokov's Cards: An AI Assisted Prewriting System to Support Bottom-Up Creative Writing
Carrera, D., Zhao, Z., Ajin Thomas, A., & Wigdor, D. (2025) · Proceedings of the 2025 Conference on Creativity and Cognition · doi:10.1145/3698061.3726916 · Cited by 6
Helpful or Harmful? Exploring the Efficacy of Large Language Models for Online Grooming Prevention
Prosser, E., & Edwards, M. (2024) · European Interdisciplinary Cybersecurity Conference · doi:10.1145/3655693.3655694 · Cited by 6
Metadata
- Authors
- Brade, S., Wang, B., Sousa, M., Oore, S., & Grossman, T.
- Affiliations
- Computer Science, University of Toronto, Canada
- Department of Computer Science, University of Toronto, Canada
- Faculty of Computer Science, Dalhousie University, Canada
- Venue
- Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology
- Publisher
- ACM
- Published
- 2023-10-29
- Pages
- 1-14
- Cited by
- 198
- References
- 9
- Funders
- National Sciences and Engineering Research Council of Canada (NSERC) (Grant IRCPJ 545100 - 18)
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
