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cs.CL2024
Kwai-STaR: Transform LLMs into State-Transition Reasoners
Xingyu Lu, Yuhang Hu, Changyi Liu +12
Mathematical reasoning presents a significant challenge to the cognitive capabilities of LLMs. Various methods have been proposed to enhance the mathematical ability of LLMs. Howev…
cs.CV2024
EVLM: An Efficient Vision-Language Model for Visual Understanding
Kaibing Chen, Dong Shen, Hanwen Zhong +14
In the field of multi-modal language models, the majority of methods are built on an architecture similar to LLaVA. These models use a single-layer ViT feature as a visual prompt,…
cs.CV2024
CoMM: A Coherent Interleaved Image-Text Dataset for Multimodal Understanding and Generation
Wei Chen, Lin Li, Yongqi Yang +5
Interleaved image-text generation has emerged as a crucial multimodal task, aiming at creating sequences of interleaved visual and textual content given a query. Despite notable ad…