1 citations · 1 across the 3 of their papers we have counts for
3 papers
Xe-Forge: Multi-Stage LLM-Powered Kernel Optimization for Intel GPU
Marcin Spoczynski, Daniel Fleischer, Moshe Berchansky +5
Porting deep learning algorithms to new hardware accelerators requires developers to repeatedly apply the same low-level optimizations -- quantization, memory access coalescing, ti…
SQuARE: Sequential Question Answering Reasoning Engine for Enhanced Chain-of-Thought in Large Language Models
Daniel Fleischer, Moshe Berchansky, Gad Markovits +1
In the rapidly evolving field of Natural Language Processing, Large Language Models (LLMs) are tasked with increasingly complex reasoning challenges. Traditional methods like chain…
HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly
Howard Yen, Tianyu Gao, Minmin Hou +5
Many benchmarks exist for evaluating long-context language models (LCLMs), yet developers often rely on synthetic tasks such as needle-in-a-haystack (NIAH) or an arbitrary subset o…