19 citations · 70 across the 9 of their papers we have counts for
10 papers
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time
Zichang Liu, Jue Wang, Tri Dao +8
Large language models (LLMs) with hundreds of billions of parameters have sparked a new wave of exciting AI applications. However, they are computationally expensive at inference t…
RLCD: Reinforcement Learning from Contrastive Distillation for Language Model Alignment
Kevin Yang, Dan Klein, Asli Celikyilmaz +2
We propose Reinforcement Learning from Contrastive Distillation (RLCD), a method for aligning language models to follow principles expressed in natural language (e.g., to be more h…
Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information
Arman Zharmagambetov, Brandon Amos, Aaron Ferber +3
Recent works in learning-integrated optimization have shown promise in settings where the optimization problem is only partially observed or where general-purpose optimizers perfor…
EurNet: Efficient Multi-Range Relational Modeling of Spatial Multi-Relational Data
Minghao Xu, Yuanfan Guo, Yi Xu +3
Modeling spatial relationship in the data remains critical across many different tasks, such as image classification, semantic segmentation and protein structure understanding. Pre…
Re3: Generating Longer Stories With Recursive Reprompting and Revision
Kevin Yang, Yuandong Tian, Nanyun Peng +1
We consider the problem of automatically generating longer stories of over two thousand words. Compared to prior work on shorter stories, long-range plot coherence and relevance ar…
DreamShard: Generalizable Embedding Table Placement for Recommender Systems
Daochen Zha, Louis Feng, Qiaoyu Tan +6
We study embedding table placement for distributed recommender systems, which aims to partition and place the tables on multiple hardware devices (e.g., GPUs) to balance the comput…