492 citations · 722 across the 9 of their papers we have counts for
9 papers
BLESS: Benchmarking Large Language Models on Sentence Simplification
Tannon Kew, Alison Chi, Laura Vásquez-Rodríguez +4
We present BLESS, a comprehensive performance benchmark of the most recent state-of-the-art large language models (LLMs) on the task of text simplification (TS). We examine how wel…
GPT-NeoX-20B: An Open-Source Autoregressive Language Model
Sid Black, Stella Biderman, Eric Hallahan +14
We introduce GPT-NeoX-20B, a 20 billion parameter autoregressive language model trained on the Pile, whose weights will be made freely and openly available to the public through a…
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Leo Gao, Stella Biderman, Sid Black +9
Recent work has demonstrated that increased training dataset diversity improves general cross-domain knowledge and downstream generalization capability for large-scale language mod…
Value Function Based Performance Optimization of Deep Learning Workloads
Benoit Steiner, Chris Cummins, Horace He +1
As machine learning techniques become ubiquitous, the efficiency of neural network implementations is becoming correspondingly paramount. Frameworks, such as Halide and TVM, separa…
Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
Qian Huang, Horace He, Abhay Singh +2
Graph Neural Networks (GNNs) are the predominant technique for learning over graphs. However, there is relatively little understanding of why GNNs are successful in practice and wh…
Better Set Representations For Relational Reasoning
Qian Huang, Horace He, Abhay Singh +3
Incorporating relational reasoning into neural networks has greatly expanded their capabilities and scope. One defining trait of relational reasoning is that it operates on a set o…
Enhancing Adversarial Example Transferability with an Intermediate Level Attack
Qian Huang, Isay Katsman, Horace He +3
Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool trained models. Adversarial examples often exhibit black-box transfer, meaning that adversa…
Adversarial Example Decomposition
Horace He, Aaron Lou, Qingxuan Jiang +3
Research has shown that widely used deep neural networks are vulnerable to carefully crafted adversarial perturbations. Moreover, these adversarial perturbations often transfer acr…
Intermediate Level Adversarial Attack for Enhanced Transferability
Qian Huang, Zeqi Gu, Isay Katsman +5
Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool trained models. Adversarial examples often exhibit black-box transfer, meaning that adversa…