BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis (2023.emnlp-industry)
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| Challenge: | Recent text-to-image models require multiple passes of prompt engineering by humans to produce satisfactory results for real-world applications. |
| Approach: | They propose a deep generative model to generate high-quality prompts from raw descriptions using visual feedback. |
| Outcome: | The proposed model produces high-quality prompts from simple raw descriptions . it can be integrated to a cloud-native AI platform to provide better image generation service in the cloud. |
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| Challenge: | Recent advances in text-to-image diffusion models have made it difficult to obtain high-quality images. |
| Approach: | They propose an adaptive framework that automatically enhances a user's prompt to improve the quality of generation models. |
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| Challenge: | Existing methods for rewriting text-to-image models require specialized vocabulary . a new approach uses large vision language models to optimize text-based models . |
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| Challenge: | Text-to-image generation models are powerful but difficult to use. Users craft specific prompts to get better images, though the prompts can be repetitive. |
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| Challenge: | Recent advances in diffusion models have enabled high-quality image generation . generating images with desired details requires proper prompts . |
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Taming Text-to-Image Synthesis for Novices: User-centric Prompt Generation via Multi-turn Guidance (2025.emnlp-main)
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Yilun Liu, Minggui He, Feiyu Yao, Yuhe Ji, Shimin Tao, Jingzhou Du, Justin Li, Jian Gao, Zhang Li, Hao Yang, Boxing Chen, Osamu Yoshie
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Prompt Refinement with Image Pivot for Text-to-Image Generation (2024.acl-long)
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| Challenge: | Recent advances in text-to-image generation have markedly expanded the boundaries of digital artistry, enabling the creation of visually compelling images with unprecedented ease. |
| Approach: | They propose to decompose the prompt refinement process into two tasks: inferring user-preferred images from user languages and translating them into system languages. |
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| Challenge: | PromptSculptor automates the iterative prompt optimization process for Text-to-Image models . previous work focused on generating detailed, high-quality prompts based on user feedback . |
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| Challenge: | a novel method to align Large Language Models to "chat" with prompt-as-input Text-to-Image Synthesis models is proposed . a user-specified instruction can be used to create a high quality image . |
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SynPrompt: Syntax-aware Enhanced Prompt Engineering for Aspect-based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Existing methods of prompt-tuning for Aspect-based Sentiment Analysis (ABSA) are crude and simple. |
| Approach: | They propose a Syntax-aware Enhanced Prompt method which mines syntactic information related to aspect words from the syntaktic dependency tree. |
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Universal Prompt Optimizer for Safe Text-to-Image Generation (2024.naacl-long)
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| Challenge: | Existing studies based on image checker, model fine-tuning and embedding blocking are impractical in real-world applications. |
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