19 citations · 40 across the 7 of their papers we have counts for
7 papers
Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation
Ruiyang Ren, Yuhao Wang, Yingqi Qu +6
Large language models (LLMs) have shown impressive prowess in solving a wide range of tasks with world knowledge. However, it remains unclear how well LLMs are able to perceive the…
ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation
Bin Shan, Yaqian Han, Weichong Yin +5
Recent cross-lingual cross-modal works attempt to extend Vision-Language Pre-training (VLP) models to non-English inputs and achieve impressive performance. However, these models f…
Clip-Tuning: Towards Derivative-free Prompt Learning with a Mixture of Rewards
Yekun Chai, Shuohuan Wang, Yu Sun +3
Derivative-free prompt learning has emerged as a lightweight alternative to prompt tuning, which only requires model inference to optimize the prompts. However, existing work did n…
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding
Qiming Peng, Yinxu Pan, Wenjin Wang +12
Recent years have witnessed the rise and success of pre-training techniques in visually-rich document understanding. However, most existing methods lack the systematic mining and u…
ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with Knowledge-Enhanced Mixture-of-Denoising-Experts
Zhida Feng, Zhenyu Zhang, Xintong Yu +12
Recent progress in diffusion models has revolutionized the popular technology of text-to-image generation. While existing approaches could produce photorealistic high-resolution im…
ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training
Bin Shan, Weichong Yin, Yu Sun +3
Recent Vision-Language Pre-trained (VLP) models based on dual encoder have attracted extensive attention from academia and industry due to their superior performance on various cro…
Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-Learning
Fan Wang, Hao Tian, Haoyi Xiong +5
Artificial neural networks (ANNs) are typically confined to accomplishing pre-defined tasks by learning a set of static parameters. In contrast, biological neural networks (BNNs) c…