no code implementations • 28 May 2024 • Qi Sun, Zhiyang Guo, Ziyu Wan, Jing Nathan Yan, Shengming Yin, Wengang Zhou, Jing Liao, Houqiang Li
In recent years, the increasing demand for dynamic 3D assets in design and gaming applications has given rise to powerful generative pipelines capable of synthesizing high-quality 4D objects.
no code implementations • 24 Jan 2024 • Junxiong Wang, Tushaar Gangavarapu, Jing Nathan Yan, Alexander M. Rush
We propose MambaByte, a token-free adaptation of the Mamba SSM trained autoregressively on byte sequences.
no code implementations • 30 Nov 2023 • Jing Nathan Yan, Jiatao Gu, Alexander M. Rush
In recent advancements in high-fidelity image generation, Denoising Diffusion Probabilistic Models (DDPMs) have emerged as a key player.
no code implementations • 14 Nov 2023 • Jing Nathan Yan, Tianqi Liu, Justin T Chiu, Jiaming Shen, Zhen Qin, Yue Yu, Yao Zhao, Charu Lakshmanan, Yair Kurzion, Alexander M. Rush, Jialu Liu, Michael Bendersky
Comparative reasoning plays a crucial role in text preference prediction; however, large language models (LLMs) often demonstrate inconsistencies in their reasoning.
no code implementations • 13 Nov 2023 • Yue Yu, Jiaming Shen, Tianqi Liu, Zhen Qin, Jing Nathan Yan, Jialu Liu, Chao Zhang, Michael Bendersky
To fully unleash the power of explanations, we propose EASE, an Explanation-Aware Soft Ensemble framework to empower in-context learning with LLMs.
1 code implementation • 1 Jun 2023 • Wang-Chiew Tan, Jane Dwivedi-Yu, Yuliang Li, Lambert Mathias, Marzieh Saeidi, Jing Nathan Yan, Alon Y. Halevy
We describe a set of experiments on TimelineQA with several state-of-the-art QA models.
1 code implementation • 20 Dec 2022 • Junxiong Wang, Jing Nathan Yan, Albert Gu, Alexander M. Rush
Even so, BiGS is able to match BERT pretraining accuracy on GLUE and can be extended to long-form pretraining of 4096 tokens without approximation.