4 citations · 4 across the 2 of their papers we have counts for
3 papers
cs.CL2026
MADS: Model-Aware Diverse Core Set Selection for Instruction Tuning
Yi Bai, Wenhao Zhang, Yao Chen +3
Instruction fine-tuning is employed to enhance the instruction-following ability of large language models (LLMs). As the amount of instruction fine-tuning data increases, selecting…
cs.CV2023★ 4 cited
Reformulating Vision-Language Foundation Models and Datasets Towards Universal Multimodal Assistants
Tianyu Yu, Jinyi Hu, Yuan Yao +10
Recent Multimodal Large Language Models (MLLMs) exhibit impressive abilities to perceive images and follow open-ended instructions. The capabilities of MLLMs depend on two crucial…
cs.CL2023
Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages
Jinyi Hu, Yuan Yao, Chongyi Wang +13
Recently there has been a significant surge in multimodal learning in terms of both image-to-text and text-to-image generation. However, the success is typically limited to English…