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Well-designed prompts are crucial for enhancing Large language models' (LLMs) reasoning capabilities while aligning their outputs with task requirements across diverse domains. However, manually designed prompts require expertise and…

Computation and Language · Computer Science 2025-08-22 Jinyu Xiang , Jiayi Zhang , Zhaoyang Yu , Xinbing Liang , Fengwei Teng , Jinhao Tu , Fashen Ren , Xiangru Tang , Sirui Hong , Chenglin Wu , Yuyu Luo

Artificial Intelligence (AI) advancement is heavily dependent on access to large-scale, high-quality training data. However, in specialized domains such as healthcare, data acquisition faces significant constraints due to privacy…

Human-Computer Interaction · Computer Science 2025-02-11 Nina Freise , Marius Heitlinger , Ruben Nuredini , Gerrit Meixner

Pre-trained vision-language models like CLIP have remarkably adapted to various downstream tasks. Nonetheless, their performance heavily depends on the specificity of the input text prompts, which requires skillful prompt template…

Machine Learning · Computer Science 2024-10-22 Yingjun Du , Wenfang Sun , Cees G. M. Snoek

Sign Language Production (SLP) is the process of converting the complex input text into a real video. Most previous works focused on the Text2Gloss, Gloss2Pose, Pose2Vid stages, and some concentrated on Prompt2Gloss and Text2Avatar stages.…

Computer Vision and Pattern Recognition · Computer Science 2026-02-03 Sen Fang , Yalin Feng , Hongbin Zhong , Yanxin Zhang , Dimitris N. Metaxas

Purpose: In the field of vulnerability repair, previous research has leveraged pretrained models and LLM-based prompt engineering, among which LLM-based approaches show better generalizability and achieve the best performance. However, the…

Software Engineering · Computer Science 2025-12-23 Ruoke Wang , Zongjie Li , Cuiyun Gao , Chaozheng Wang , Yang Xiao , Xuan Wang

Text-to-image (T2I) models can generate not-safe-for-work (NSFW) content, motivating multi-stage safety pipelines with both text and image filters. Newer LLM-based filters detect latent intent beyond keywords, making token-level…

Machine Learning · Computer Science 2026-05-26 Zixuan Chen , Hao Lin , Ke Xu , Xinghao Jiang , Tanfeng Sun

The recent emergence of multimodal large language models (LLMs) has introduced new opportunities for improving visual hazard recognition on construction sites. Unlike traditional computer vision models that rely on domain-specific training…

Computer Vision and Pattern Recognition · Computer Science 2026-05-12 Nishi Chaudhary , S M Jamil Uddin , Sathvik Sharath Chandra , Anto Ovid , Alex Albert

Recent advances in deep learning, such as powerful generative models and joint text-image embeddings, have provided the computational creativity community with new tools, opening new perspectives for artistic pursuits. Text-to-image…

Computer Vision and Pattern Recognition · Computer Science 2022-04-20 Yingtao Tian , Marco Cuturi , David Ha

The rapid advancement of Text-to-Image(T2I) generative models has enabled the synthesis of high-quality images guided by textual descriptions. Despite this significant progress, these models are often susceptible in generating contents that…

Computer Vision and Pattern Recognition · Computer Science 2024-06-25 Yichen Sun , Zhixuan Chu , Zhan Qin , Kui Ren

Generative text-to-image models have gained great popularity among the public for their powerful capability to generate high-quality images based on natural language prompts. However, developing effective prompts for desired images can be…

Artificial Intelligence · Computer Science 2023-11-02 Yingchaojie Feng , Xingbo Wang , Kam Kwai Wong , Sijia Wang , Yuhong Lu , Minfeng Zhu , Baicheng Wang , Wei Chen

The text-to-image synthesis by diffusion models has recently shown remarkable performance in generating high-quality images. Although performs well for simple texts, the models may get confused when faced with complex texts that contain…

Computer Vision and Pattern Recognition · Computer Science 2024-01-15 Chang Yu , Junran Peng , Xiangyu Zhu , Zhaoxiang Zhang , Qi Tian , Zhen Lei

Text-to-image models have made significant strides, producing impressive results in generating images from textual descriptions. However, creating a scalable pipeline for deploying these models in production remains a challenge. Achieving…

Computer Vision and Pattern Recognition · Computer Science 2026-02-17 Parmida Atighehchian , Henry Wang , Andrei Kapustin , Boris Lerner , Tiancheng Jiang , Taylor Jensen , Negin Sokhandan

Large language models (LLMs) enable system builders today to create competent NLP systems through prompting, where they only need to describe the task in natural language and provide a few examples. However, in other ways, LLMs are a step…

Computation and Language · Computer Science 2023-08-24 Vijay Viswanathan , Chenyang Zhao , Amanda Bertsch , Tongshuang Wu , Graham Neubig

Large Language Models (LLMs) have shown strong performance across a wide range of natural language processing tasks; however, their effectiveness is highly dependent on prompt design, structure, and embedded reasoning signals. Conventional…

Machine Learning · Computer Science 2026-04-07 Shiek Ruksana , Sailesh Kiran Kurra , Thipparthi Sanjay Baradwaj

Images as an artistic medium often rely on specific camera angles and lens distortions to convey ideas or emotions; however, such precise control is missing in current text-to-image models. We propose an efficient and general solution that…

Computer Vision and Pattern Recognition · Computer Science 2025-01-23 Edurne Bernal-Berdun , Ana Serrano , Belen Masia , Matheus Gadelha , Yannick Hold-Geoffroy , Xin Sun , Diego Gutierrez

This paper targets to enhance the diffusion-based text-to-video generation by improving the two input prompts, including the noise and the text. Accommodated with this goal, we propose POS, a training-free Prompt Optimization Suite to boost…

Computer Vision and Pattern Recognition · Computer Science 2024-06-11 Shijie Ma , Huayi Xu , Mengjian Li , Weidong Geng , Yaxiong Wang , Meng Wang

Large Language Models, particularly decoder-only generative models such as GPT, are increasingly used to automate Software Engineering tasks. These models are primarily guided through natural language prompts, making prompt engineering a…

Software Engineering · Computer Science 2026-01-06 Alexander Korn , Lea Zaruchas , Chetan Arora , Andreas Metzger , Sven Smolka , Fanyu Wang , Andreas Vogelsang

While text-to-image (T2I) models can synthesize high-quality images, their performance degrades significantly when prompted with novel or out-of-distribution (OOD) entities due to inherent knowledge cutoffs. We introduce World-To-Image, a…

Computer Vision and Pattern Recognition · Computer Science 2025-10-07 Moo Hyun Son , Jintaek Oh , Sun Bin Mun , Jaechul Roh , Sehyun Choi

With the rapid development of Artificial Intelligence Generated Content (AIGC), it has become a common practice to train models on synthetic data due to data-scarcity and privacy leakage problems. Owing to massive and diverse information…

Computer Vision and Pattern Recognition · Computer Science 2025-06-24 Shiye Lei , Hao Chen , Sen Zhang , Bo Zhao , Dacheng Tao

Large Language Models (LLMs) are machine learning models that have seen widespread adoption due to their capability of handling previously difficult tasks. LLMs, due to their training, are sensitive to how exactly a question is presented,…

Software Engineering · Computer Science 2025-12-22 Jae Yong Lee , Sungmin Kang , Shin Yoo