Elamur Research⌕
☰
home / papers / 2025 / T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts

T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts

Paper · 2025 · Ziwei Huang, Wanggui He, Quanyu Long et al.

Summary

Most text-to-image (T2I) evaluation measures prompt alignment, image quality or object composition. It rarely checks whether models depict specific real-world entities correctly, such as a particular handbag model, landmark or dog breed. To fill this gap, the authors built T2I-FactualBench, which they describe as the largest factuality benchmark by number of concepts and prompts. They drew entity-grounded categories from CNER and kept "knowledge concepts," meaning items with few hyponyms in BabelNet. Prompts are arranged in three tiers of increasing difficulty, from reproducing a single concept to combining several concepts in one scene. Scoring uses a multi-round VQA protocol: an MLLM compares each output with a reference image, checks whether requested variations are rendered, and then judges multi-concept compositions. Seven open and closed models were tested, including Stable Diffusion variants, Flux.1 and DALLE-3. Even the strongest models fall well short, especially on compositions. The benchmark gives a concrete, human-validated way to measure knowledge errors that CLIP-style metrics overlook.

Key points

  • Concept selection: Concepts come from 8 of CNER's 29 categories (e.g., animal, artifact, food, location, person, celestial). A concept qualifies if BabelNet lists fewer than four hyponyms for it.
  • Three-tier design: Tasks run from the SKCM level (single-concept memorization) to MKCC (multi-concept composition), the hardest tier.
  • Evaluator choice: GPT-4o was selected as the judge because it agreed best with human ratings, reaching 81% accuracy on Instantiation Completeness. Qwen2.5-VL-72B and InternVL-2.5-78B were competitive open alternatives. Dropping reference images clearly reduced agreement with humans.
  • General-concept ablation: On 100 MKCC prompts, generic substitutes (e.g., "dog" instead of "Basset hound dog") raised scores substantially. For SD 3.5, concept factuality rose from 65.0 to 85.3.
  • Weak domains and knowledge injection: Models perform worst on plants and locations. Typical errors include wrong look-alike concepts and features blended between entities. Supplying reference images or text descriptions helps only partly.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 works

🧭 Topics 1Research subjects and languages the work is about.Vision & language · 657 works

Cite

Cite as Ziwei Huang, Wanggui He, Quanyu Long, Yandi Wang, Haoyuan Li, Zhelun Yu, Fangxun Shu, Weilong Dai, Hao Jiang, Fei Wu and Leilei Gan. 2025. T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.acl-long.1334

@inproceedings{huang2025t2i,
  title = {{T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts}},
  author = {Ziwei Huang, Wanggui He, Quanyu Long, Yandi Wang, Haoyuan Li, Zhelun Yu, Fangxun Shu, Weilong Dai, Hao Jiang, Fei Wu and Leilei Gan},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.1334},
  url = {https://doi.org/10.18653/v1/2025.acl-long.1334}
}

References

66 references, 14 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Dominik Lorenz Andreas Blattmann, Axel Sauer. 2024. Flux.1. find
  2. Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. 2023. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308.12966.
  3. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al. 2025. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.13923. — Qwen2.5-VL Technical Report
  4. James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al. 2023. Improving image generation with better captions. Computer Science. https://cdn.openai. com/papers/dall-e-3. pdf, 2(3):8. ↗
  5. Jinhe Bi, Yifan Wang, Danqi Yan, Xun Xiao, Artur Hecker, Volker Tresp, and Yunpu Ma. 2025. Prism: Self-pruning intrinsic selection method for training-free multimodal data selection. arXiv preprint arXiv:2502.12119. — [arXiv:2502.12119](https://arxiv.org/abs/2502.12119) find
  6. Jinhe Bi, Yujun Wang, Haokun Chen, Xun Xiao, Artur Hecker, Volker Tresp, and Yunpu Ma. 2024. Visual instruction tuning with 500x fewer parameters through modality linear representation-steering. arXiv preprint arXiv:2412.12359. — [arXiv:2412.12359](https://arxiv.org/abs/2412.12359) find
  7. Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. 2021. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9650–9660.
  8. Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Zhongdao Wang, James T. Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li. 2024a. Pixart-α: Fast training of diffusion transformer for photorealistic text-to-image synthesis. In The Twelfth International Conference on Learning Representations, I… find
  9. Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al. 2024b. Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern…
  10. Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach. 2024. Scaling rectified flow transformers for high-r… — [arXiv:2403.03206](https://arxiv.org/abs/2403.03206) find
  11. Xingyu Fu, Muyu He, Yujie Lu, William Yang Wang, and Dan Roth. 2024. Commonsense-t2i challenge: Can text-to-image generation models understand commonsense? arXiv preprint arXiv:2406.07546. find
  12. Sebastian Hartwig, Dominik Engel, Leon Sick, Hannah Kniesel, Tristan Payer, Timo Ropinski, et al. 2024. Evaluating text to image synthesis: Survey and taxonomy of image quality metrics. arXiv preprint arXiv:2403.11821. — [arXiv:2403.11821](https://arxiv.org/abs/2403.11821) find
  13. Wanggui He, Siming Fu, Mushui Liu, Xierui Wang, Wenyi Xiao, Fangxun Shu, Yi Wang, Lei Zhang, Zhelun Yu, Haoyuan Li, et al. 2024. Mars: Mixture of auto-regressive models for fine-grained text-to-image synthesis. arXiv preprint arXiv:2407.07614. find
  14. Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. 2021. Clipscore: A reference-free evaluation metric for image captioning. arXiv preprint arXiv:2104.08718.
  15. Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30. find
  16. Yushi Hu, Benlin Liu, Jungo Kasai, Yizhong Wang, Mari Ostendorf, Ranjay Krishna, and Noah A Smith. 2023. Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 20406–20417. find
  17. Hsin-Ping Huang, Xinyi Wang, Yonatan Bitton, Hagai Taitelbaum, Gaurav Singh Tomar, Ming-Wei Chang, Xuhui Jia, Kelvin CK Chan, Hexiang Hu, Yu-Chuan Su, et al. 2024. Kitten: A knowledge-intensive evaluation of image generation on visual entities. arXiv preprint arXiv:2410.11824. — [arXiv:2410.11824](https://arxiv.org/abs/2410.11824) find
  18. Kaiyi Huang, Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu. 2023. T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation. Advances in Neural Information Processing Systems, 36:78723–78747. find
  19. Nasiba Komil Qizi Jumaeva. 2024. Using the hyponyms for improving the beginners’vocabulary range. Academic research in educational sciences, 5(CSPU Conference 1):669–673. find
  20. Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy. 2023. Pick-a-pic: An open dataset of user preferences for text-to-image generation. Advances in Neural Information Processing Systems, 36:36652–36663. find
  21. Klaus Krippendorff. 2018. Content analysis: An introduction to its methodology. Sage publications. find
  22. Max Ku, Dongfu Jiang, Cong Wei, Xiang Yue, and Wenhu Chen. 2023a. Viescore: Towards explainable metrics for conditional image synthesis evaluation. arXiv preprint arXiv:2312.14867.
  23. Max Ku, Tianle Li, Kai Zhang, Yujie Lu, Xingyu Fu, Wenwen Zhuang, and Wenhu Chen. 2023b. Im-agenhub: Standardizing the evaluation of conditional image generation models. arXiv preprint arXiv:2310.01596. — [arXiv:2310.01596](https://arxiv.org/abs/2310.01596) find
  24. Tony Lee, Michihiro Yasunaga, Chenlin Meng, Yifan Mai, Joon Sung Park, Agrim Gupta, Yunzhi Zhang, Deepak Narayanan, Hannah Teufel, Marco Bellagente, et al. 2024. Holistic evaluation of text-to-image models. Advances in Neural Information Processing Systems, 36. find
  25. Baiqi Li, Zhiqiu Lin, Deepak Pathak, Jiayao Li, Yixin Fei, Kewen Wu, Tiffany Ling, Xide Xia, Pengchuan Zhang, Graham Neubig, et al. 2024a. Genai-bench: Evaluating and improving compositional text-to-visual generation. arXiv preprint arXiv:2406.13743. find
  26. Baiqi Li, Zhiqiu Lin, Deepak Pathak, Jiayao Li, Yixin Fei, Kewen Wu, Xide Xia, Pengchuan Zhang, Graham Neubig, and Deva Ramanan. 2024b. Evaluating and improving compositional text-to-visual generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5290–5… find
  27. Daiqing Li, Aleks Kamko, Ehsan Akhgari, Ali Sabet, Linmiao Xu, and Suhail Doshi. 2024c. Playground v2.5: Three insights towards enhancing aesthetic quality in text-to-image generation. CoRR, abs/2402.17245. — [arXiv:2402.17245](https://arxiv.org/abs/2402.17245) find
  28. Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. 2022. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International conference on machine learning, pages 12888–12900. PMLR.
  29. Tianwei Lin, Jiang Liu, Wenqiao Zhang, Zhaocheng Li, Yang Dai, Haoyuan Li, Zhelun Yu, Wanggui He, Juncheng Li, Hao Jiang, et al. 2024. Teamlora: Boosting low-rank adaptation with expert collaboration and competition. arXiv preprint arXiv:2408.09856. find
  30. Jiang Liu, Bolin Li, Haoyuan Li, Tianwei Lin, Wenqiao Zhang, Tao Zhong, Zhelun Yu, Jinghao Wei, Hao Cheng, Wanggui He, et al. 2024. Boosting private domain understanding of efficient mllms: A tuning-free, adaptive, universal prompt optimization framework. arXiv preprint arXiv:2412.19684. — [arXiv:2412.19684](https://arxiv.org/abs/2412.19684) find
  31. Mushui Liu, Yuhang Ma, Zhen Yang, Jun Dan, Yunlong Yu, Zeng Zhao, Zhipeng Hu, Bai Liu, and Changjie Fan. 2025. Llm4gen: Leveraging semantic representation of llms for text-to-image generation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 5523–5531. find
  32. Yujie Lu, Dongfu Jiang, Wenhu Chen, William Wang, Yejin Choi, and Bill Yuchen Lin. 2024a. Wildvision arena: Benchmarking multimodal llms in the wild (february 2024). URL https://huggingface.co/spaces/WildVision/vision-arena. ↗
  33. Yujie Lu, Xianjun Yang, Xiujun Li, Xin Eric Wang, and William Yang Wang. 2024b. Llmscore: Unveiling the power of large language models in text-to-image synthesis evaluation. Advances in Neural Information Processing Systems, 36. find
  34. Giuliano Martinelli, Francesco Molfese, Simone Tedeschi, Alberte Fernández-Castro, and Roberto Navigli. 2024. Cner: Concept and named entity recognition. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies… find
  35. Fanqing Meng, Wenqi Shao, Lixin Luo, Yahong Wang, Yiran Chen, Quanfeng Lu, Yue Yang, Tianshuo Yang, Kaipeng Zhang, Yu Qiao, et al. 2024. Phybench: A physical commonsense benchmark for evaluating text-to-image models. arXiv preprint arXiv:2406.11802. — [arXiv:2406.11802](https://arxiv.org/abs/2406.11802) find
  36. Roberto Navigli and Simone Paolo Ponzetto. 2012. Babelnet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artificial intelligence, 193:217–250. find
  37. OpenAI. 2024. Chatgpt. — GPT-4o System Card https://openai.com/ index/gpt-4o-system-card/. ↗
  38. Jonas Oppenlaender. 2022. The creativity of text-to-image generation. In Proceedings of the 25th international academic mindtrek conference, pages 192– 202. find
  39. Dong Huk Park, Samaneh Azadi, Xihui Liu, Trevor Darrell, and Anna Rohrbach. 2021. Benchmark for compositional text-to-image synthesis. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1). find
  40. Yuang Peng, Yuxin Cui, Haomiao Tang, Zekun Qi, Runpei Dong, Jing Bai, Chunrui Han, Zheng Ge, Xiangyu Zhang, and Shu-Tao Xia. 2024. Dreambench++: A human-aligned benchmark for personalized image generation. arXiv preprint arXiv:2406.16855. — [arXiv:2406.16855](https://arxiv.org/abs/2406.16855) find
  41. LA, USA, June 18-24, 2022, pages 10674–10685. IEEE. find
  42. Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. 2022. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural information processing syste…
  43. Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. 2016. Improved techniques for training gans. Advances in neural information processing systems, 29.
  44. D She, Mushui Liu, Jingxuan Pang, Jin Wang, Zhen Yang, Wanggui He, Guanghao Zhang, Yi Wang, Qihan Huang, Haobin Tang, et al. 2025. Customvideox: 3d reference attention driven dynamic adaptation for zero-shot customized video diffusion transformers. arXiv preprint arXiv:2502.06527. — [arXiv:2502.06527](https://arxiv.org/abs/2502.06527) find
  45. Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. 2024a. Autoregressive model beats diffusion: Llama for scalable image generation. arXiv preprint arXiv:2406.06525. — [arXiv:2406.06525](https://arxiv.org/abs/2406.06525) find
  46. Quan Sun, Yufeng Cui, Xiaosong Zhang, Fan Zhang, Qiying Yu, Yueze Wang, Yongming Rao, Jingjing Liu, Tiejun Huang, and Xinlong Wang. 2024b. Generative multimodal models are in-context learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14398– 14409. find
  47. Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. 2023. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805.
  48. Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. 2024. SDXL: improving latent diffusion models for high-resolution image synthesis. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, Ma…
  49. Shanu Vashishtha, Abhinav Prakash, Lalitesh Morishetti, Kaushiki Nag, Yokila Arora, Sushant Kumar, and Kannan Achan. 2024. Chaining text-to-image and large language model: A novel approach for generating personalized e-commerce banners. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge D… find
  50. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PM…
  51. Veera Vimpari, Annakaisa Kultima, Perttu Hämäläinen, and Christian Guckelsberger. 2023. “an adaptor-die type of situation”: Perception, adoption, and use of text-to-image-generation ai by game industry professionals. Proceedings of the ACM on Human-Computer Interaction, 7(CHI PLAY):131–164. find
  52. Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-resolution image synthesis with latent diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, J…
  53. Weiyun Wang, Zhangwei Gao, Lianjie Chen, Zhe Chen, Jinguo Zhu, Xiangyu Zhao, Yangzhou Liu, Yue Cao, Shenglong Ye, Xizhou Zhu, et al. 2025a. Visualprm: An effective process reward model for multimodal reasoning. arXiv preprint arXiv:2503.10291. — [arXiv:2503.10291](https://arxiv.org/abs/2503.10291) find
  54. Xinlu Zhang, Yujie Lu, Weizhi Wang, An Yan, Jun Yan, Lianke Qin, Heng Wang, Xifeng Yan, William Yang Wang, and Linda Ruth Petzold. 2023a. Gpt-4v (ision) as a generalist evaluator for vision-language tasks. arXiv preprint arXiv:2311.01361. — [arXiv:2311.01361](https://arxiv.org/abs/2311.01361) find
  55. X. Wang, Siming Fu, Qihan Huang, Wanggui He, and Hao Jiang. 2024b. Ms-diffusion: Multi-subject zero-shot image personalization with layout guidance. CoRR, abs/2406.07209. — [arXiv:2406.07209](https://arxiv.org/abs/2406.07209) find
  56. Yuxuan Zhang, Jiaming Liu, Yiren Song, Rui Wang, Hao Tang, Jinpeng Yu, Huaxia Li, Xu Tang, Yao Hu, Han Pan, and Zhongliang Jing. 2023b. Ssr-encoder: Encoding selective subject representation for subject-driven generation. CoRR, abs/2312.16272. — [arXiv:2312.16272](https://arxiv.org/abs/2312.16272) find
  57. Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, et al. 2024c. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409.18869. — [arXiv:2409.18869](https://arxiv.org/abs/2409.18869) find
  58. Yi Wang, Mushui Liu, Wanggui He, Longxiang Zhang, Ziwei Huang, Guanghao Zhang, Fangxun Shu, Zhong Tao, Dong She, Zhelun Yu, et al. 2025b. Mint: Multi-modal chain of thought in unified generative models for enhanced image generation. arXiv preprint arXiv:2503.01298. — [arXiv:2503.01298](https://arxiv.org/abs/2503.01298) find
  59. Ziyu Zhao, Leilei Gan, Guoyin Wang, Wangchunshu Zhou, Hongxia Yang, Kun Kuang, and Fei Wu. 2024. Loraretriever: Input-aware lora retrieval and composition for mixed tasks in the wild. arXiv preprint arXiv:2402.09997. find
  60. Tong Wu, Guandao Yang, Zhibing Li, Kai Zhang, Ziwei Liu, Leonidas Guibas, Dahua Lin, and Gordon Wetzstein. 2024a. Gpt-4v (ision) is a human-aligned evaluator for text-to-3d generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22227–22238. find
  61. Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. 2023. Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis. arXiv preprint arXiv:2306.09341. — [arXiv:2306.09341](https://arxiv.org/abs/2306.09341) find
  62. Xindi Wu, Dingli Yu, Yangsibo Huang, Olga Russakovsky, and Sanjeev Arora. 2024b. Conceptmix: A compositional image generation benchmark with controllable difficulty. arXiv preprint arXiv:2408.14339. — [arXiv:2408.14339](https://arxiv.org/abs/2408.14339) find
  63. Wenyi Xiao, Ziwei Huang, Leilei Gan, Wanggui He, Haoyuan Li, Zhelun Yu, Hao Jiang, Fei Wu, and Linchao Zhu. 2024a. Detecting and mitigating hallucination in large vision language models via fine-grained ai feedback. arXiv preprint arXiv:2404.14233. — [arXiv:2404.14233](https://arxiv.org/abs/2404.14233) find
  64. Wenyi Xiao, Zechuan Wang, Leilei Gan, Shuai Zhao, Wanggui He, Luu Anh Tuan, Long Chen, Hao Jiang, Zhou Zhao, and Fei Wu. 2024b. A comprehensive survey of datasets, theories, variants, and applications in direct preference optimization. arXiv preprint arXiv:2410.15595. — [arXiv:2410.15595](https://arxiv.org/abs/2410.15595) find
  65. Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. 2024. Imagereward: Learning and evaluating human preferences for text-to-image generation. Advances in Neural Information Processing Systems, 36. find
  66. Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, et al. 2022. Scaling autoregressive models for content-rich text-to-image generation. arXiv preprint arXiv:2206.10789, 2(3):5. — [arXiv:2206.10789](https://arxiv.org/abs/2206.10789) find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/huang-2025-t2i-factualbench-benchmarking-factuality-text/