15 citations · 21 across the 4 of their papers we have counts for
5 papers
Oasis: One Image is All You Need for Multimodal Instruction Data Synthesis
Letian Zhang, Quan Cui, Bingchen Zhao +1
The success of multi-modal large language models (MLLMs) has been largely attributed to the large-scale training data. However, the training data of many MLLMs is unavailable due t…
A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?
Yunfei Xie, Juncheng Wu, Haoqin Tu +6
Large language models (LLMs) have exhibited remarkable capabilities across various domains and tasks, pushing the boundaries of our knowledge in learning and cognition. The latest…
Benchmarking Multi-Image Understanding in Vision and Language Models: Perception, Knowledge, Reasoning, and Multi-Hop Reasoning
Bingchen Zhao, Yongshuo Zong, Letian Zhang +1
The advancement of large language models (LLMs) has significantly broadened the scope of applications in natural language processing, with multi-modal LLMs extending these capabili…
What If the TV Was Off? Examining Counterfactual Reasoning Abilities of Multi-modal Language Models
Letian Zhang, Xiaotong Zhai, Zhongkai Zhao +3
Counterfactual reasoning, a fundamental aspect of human cognition, involves contemplating alternatives to established facts or past events, significantly enhancing our abilities in…
Fool Your (Vision and) Language Model With Embarrassingly Simple Permutations
Yongshuo Zong, Tingyang Yu, Ruchika Chavhan +2
Large language and vision-language models are rapidly being deployed in practice thanks to their impressive capabilities in instruction following, in-context learning, and so on. T…