2 citations · 4 across the 6 of their papers we have counts for
6 papers
The Hidden Cost of Thinking: Energy Use and Environmental Impact of LMs Beyond Pretraining
Jacob Morrison, Noah A. Smith, Emma Strubell
Modern language model development extends far beyond pretraining, yet environmental reporting remains narrowly focused on the cost of training a single final model. In this work, w…
Train Separately, Merge Together: Modular Post-Training with Mixture-of-Experts
Jacob Morrison, Sanjay Adhikesaven, Akshita Bhagia +3
Extending a fully post-trained language model with new domain capabilities is fundamentally limited by monolithic training paradigms: retraining from scratch is expensive and scale…
Olmo Hybrid: From Theory to Practice and Back
William Merrill, Yanhong Li, Tyler Romero +19
Recent work has demonstrated the potential of non-transformer language models, especially linear recurrent neural networks (RNNs) and hybrid models that mix recurrence and attentio…
Intentionally Unintentional: GenAI Exceptionalism and the First Amendment
David Atkinson, Jena D. Hwang, Jacob Morrison
This paper challenges the assumption that courts should grant First Amendment protections to outputs from large generative AI models, such as GPT-4 and Gemini. We argue that becaus…
Unsettled Law: Time to Generate New Approaches?
David Atkinson, Jacob Morrison
We identify several important and unsettled legal questions with profound ethical and societal implications arising from generative artificial intelligence (GenAI), focusing on its…
A Legal Risk Taxonomy for Generative Artificial Intelligence
David Atkinson, Jacob Morrison
For the first time, this paper presents a taxonomy of legal risks associated with generative AI (GenAI) by breaking down complex legal concepts to provide a common understanding of…