3 citations · 6 across the 5 of their papers we have counts for
5 papers
Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale
Yicheng Zou, Dongsheng Zhu, Lin Zhu +174
We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancem…
Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocation
Dongsheng Zhu, Weixian Shi, Zhengliang Shi +4
Although current Large Language Models (LLMs) exhibit impressive capabilities, performing complex real-world tasks still requires tool learning. Mainstream methods, such as CoT/ReA…
VisLingInstruct: Elevating Zero-Shot Learning in Multi-Modal Language Models with Autonomous Instruction Optimization
Dongsheng Zhu, Xunzhu Tang, Weidong Han +5
This paper presents VisLingInstruct, a novel approach to advancing Multi-Modal Language Models (MMLMs) in zero-shot learning. Current MMLMs show impressive zero-shot abilities in m…
SDA: Simple Discrete Augmentation for Contrastive Sentence Representation Learning
Dongsheng Zhu, Zhenyu Mao, Jinghui Lu +2
Contrastive learning has recently achieved compelling performance in unsupervised sentence representation. As an essential element, data augmentation protocols, however, have not b…
What Makes Pre-trained Language Models Better Zero-shot Learners?
Jinghui Lu, Dongsheng Zhu, Weidong Han +3
Current methods for prompt learning in zeroshot scenarios widely rely on a development set with sufficient human-annotated data to select the best-performing prompt template a post…