3 citations · 9 across the 21 of their papers we have counts for
8 papers · 1 filter
A Contrastive Compositional Benchmark for Text-to-Image Synthesis: A Study with Unified Text-to-Image Fidelity Metrics
Xiangru Zhu, Penglei Sun, Chengyu Wang +4
Text-to-image (T2I) synthesis has recently achieved significant advancements. However, challenges remain in the model's compositionality, which is the ability to create new combina…
Towards Better Parameter-Efficient Fine-Tuning for Large Language Models: A Position Paper
Chengyu Wang, Junbing Yan, Wei Zhang +1
This paper delves into the pressing need in Parameter-Efficient Fine-Tuning (PEFT) for Large Language Models (LLMs). While LLMs possess remarkable capabilities, their extensive par…
FastBlend: a Powerful Model-Free Toolkit Making Video Stylization Easier
Zhongjie Duan, Chengyu Wang, Cen Chen +3
With the emergence of diffusion models and rapid development in image processing, it has become effortless to generate fancy images in tasks such as style transfer and image editin…
Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension
Tingfeng Cao, Chengyu Wang, Chuanqi Tan +2
In cross-lingual language understanding, machine translation is often utilized to enhance the transferability of models across languages, either by translating the training data fr…
From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models
Junbing Yan, Chengyu Wang, Taolin Zhang +3
Reasoning is a distinctive human capacity, enabling us to address complex problems by breaking them down into a series of manageable cognitive steps. Yet, complex logical reasoning…
BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis
Tingfeng Cao, Chengyu Wang, Bingyan Liu +3
Recently, diffusion-based deep generative models (e.g., Stable Diffusion) have shown impressive results in text-to-image synthesis. However, current text-to-image models often requ…
Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding
Jianing Wang, Qiushi Sun, Nuo Chen +4
The recent success of large pre-trained language models (PLMs) heavily hinges on massive labeled data, which typically produces inferior performance in low-resource scenarios. To r…
On the Trustworthiness Landscape of State-of-the-art Generative Models: A Survey and Outlook
Mingyuan Fan, Chengyu Wang, Cen Chen +2
Diffusion models and large language models have emerged as leading-edge generative models, revolutionizing various aspects of human life. However, the practical implementations of…