84 citations · 163 across the 8 of their papers we have counts for
8 papers
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Ziyang Luo, Can Xu, Pu Zhao +7
Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated exceptional performance in code-related tasks. However, most existing models are solely pre-trained on…
Augmented Large Language Models with Parametric Knowledge Guiding
Ziyang Luo, Can Xu, Pu Zhao +5
Large Language Models (LLMs) have significantly advanced natural language processing (NLP) with their impressive language understanding and generation capabilities. However, their…
A Prompt Log Analysis of Text-to-Image Generation Systems
Yutong Xie, Zhaoying Pan, Jinge Ma +2
Recent developments in large language models (LLM) and generative AI have unleashed the astonishing capabilities of text-to-image generation systems to synthesize high-quality imag…
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Retrieval
Ziyang luo, Pu Zhao, Can Xu +6
Image-text retrieval (ITR) is a task to retrieve the relevant images/texts, given the query from another modality. The conventional dense retrieval paradigm relies on encoding imag…
Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning
Hongzhan Lin, Pengyao Yi, Jing Ma +4
The spread of rumors along with breaking events seriously hinders the truth in the era of social media. Previous studies reveal that due to the lack of annotated resources, rumors…
I-Tuning: Tuning Frozen Language Models with Image for Lightweight Image Captioning
Ziyang Luo, Zhipeng Hu, Yadong Xi +2
Image Captioning is a traditional vision-and-language task that aims to generate the language description of an image. Recent studies focus on scaling up the model size and the num…
A Frustratingly Simple Approach for End-to-End Image Captioning
Ziyang Luo, Yadong Xi, Rongsheng Zhang +1
Image Captioning is a fundamental task to join vision and language, concerning about cross-modal understanding and text generation. Recent years witness the emerging attention on i…
Coarse-To-Fine Incremental Few-Shot Learning
Xiang Xiang, Yuwen Tan, Qian Wan +1
Different from fine-tuning models pre-trained on a large-scale dataset of preset classes, class-incremental learning (CIL) aims to recognize novel classes over time without forgett…