1 citations · 1 across the 4 of their papers we have counts for
5 papers
Reflections and New Directions for Human-Centered Large Language Models
Caleb Ziems, Dora Zhao, Rose E. Wang +55
Large Language Models (LLMs) are increasingly shaping the private and professional lives of users, with numerous applications in business, education, finance, healthcare, law, and…
KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta
Gang Liao, Hongsen Qin, Ying Wang +36
Making deep learning recommendation model (DLRM) training and inference fast and efficient is important. However, this presents three key system challenges - model architecture div…
Scaling Context Requires Rethinking Attention
Carles Gelada, Jacob Buckman, Sean Zhang +1
We argue that neither transformers nor sub-quadratic architectures are well suited to training at long sequence lengths: the cost of processing the context is too expensive in the…
Conformal Transformations for Symmetric Power Transformers
Saurabh Kumar, Jacob Buckman, Carles Gelada +1
Transformers with linear attention offer significant computational advantages over softmax-based transformers but often suffer from degraded performance. The symmetric power (sympo…
Reading Subtext: Evaluating Large Language Models on Short Story Summarization with Writers
Melanie Subbiah, Sean Zhang, Lydia B. Chilton +1
We evaluate recent Large Language Models (LLMs) on the challenging task of summarizing short stories, which can be lengthy, and include nuanced subtext or scrambled timelines. Impo…