2 citations · 2 across the 3 of their papers we have counts for
6 papers
-Reasoner: LLM Reasoning via Test-Time Gradient Descent in Latent Space
Peihao Wang, Ruisi Cai, Zhen Wang +4
Scaling inference-time compute for Large Language Models (LLMs) has unlocked unprecedented reasoning capabilities. However, existing inference-time scaling methods typically rely o…
All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks
Ajay Jaiswal, Nurendra Choudhary, Ravinarayana Adkathimar +6
Graph Neural Networks (GNNs) have attracted immense attention in the past decade due to their numerous real-world applications built around graph-structured data. On the other hand…
LLaGA: Large Language and Graph Assistant
Runjin Chen, Tong Zhao, Ajay Jaiswal +2
Graph Neural Networks (GNNs) have empowered the advance in graph-structured data analysis. Recently, the rise of Large Language Models (LLMs) like GPT-4 has heralded a new era in d…
Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity
Lu Yin, You Wu, Zhenyu Zhang +10
Large Language Models (LLMs), renowned for their remarkable performance across diverse domains, present a challenge when it comes to practical deployment due to their colossal mode…
Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs "Difficult" Downstream Tasks in LLMs
Lu Yin, Ajay Jaiswal, Shiwei Liu +2
We present Junk DNA Hypothesis by adopting a novel task-centric angle for the pre-trained weights of large language models (LLMs). It has been believed that weights in LLMs contain…
Compressing LLMs: The Truth is Rarely Pure and Never Simple
Ajay Jaiswal, Zhe Gan, Xianzhi Du +3
Despite their remarkable achievements, modern Large Language Models (LLMs) face exorbitant computational and memory footprints. Recently, several works have shown significant succe…