no code implementations • 23 May 2024 • William Fleshman, Benjamin Van Durme
We introduce RE-Adapt, an approach to fine-tuning large language models on new domains without degrading any pre-existing instruction-tuning.
no code implementations • 12 Apr 2024 • William Fleshman, Aleem Khan, Marc Marone, Benjamin Van Durme
Large language models (LLMs) are increasingly capable of completing knowledge intensive tasks by recalling information from a static pretraining corpus.
no code implementations • 15 Nov 2023 • William Fleshman, Benjamin Van Durme
Character-level language models obviate the need for separately trained tokenizers, but efficiency suffers from longer sequence lengths.
1 code implementation • 17 Dec 2020 • Edward Raff, William Fleshman, Richard Zak, Hyrum S. Anderson, Bobby Filar, Mark McLean
Recent works within machine learning have been tackling inputs of ever-increasing size, with cybersecurity presenting sequence classification problems of particularly extreme lengths.
1 code implementation • 15 Jun 2018 • William Fleshman, Edward Raff, Jared Sylvester, Steven Forsyth, Mark McLean
Adversarial attacks against neural networks are a problem of considerable importance, for which effective defenses are not yet readily available.
no code implementations • 12 Jun 2018 • William Fleshman, Edward Raff, Richard Zak, Mark McLean, Charles Nicholas
As machine-learning (ML) based systems for malware detection become more prevalent, it becomes necessary to quantify the benefits compared to the more traditional anti-virus (AV) systems widely used today.