activity
20232025
most citedAlgorithmic progress in language models

13 citations · 17 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CY2025

In the Mood to Exclude: Revitalizing Trespass to Chattels in the Era of GenAI Scraping

David Atkinson

GenAI companies are strip-mining the web. Their scraping bots harvest content at an unprecedented scale, circumventing technical barriers to fuel billion-dollar models while creato…

cs.CY2025

Open Shouldn't Mean Exempt: Open-Source Exceptionalism and Generative AI

David Atkinson

Open-source status should not shield generative artificial intelligence systems from ethical or legal accountability. Through a rigorous analysis of regulatory, legal, and policy f…

cs.CY2025

Putting GenAI on Notice: GenAI Exceptionalism and Contract Law

David Atkinson

Gathering enough data to create sufficiently useful training datasets for generative artificial intelligence requires scraping most public websites. The scraping is conducted using…

cs.CY2025

Unfair Learning: GenAI Exceptionalism and Copyright Law

David Atkinson

This paper challenges the argument that generative artificial intelligence (GenAI) is entitled to broad immunity from copyright law for reproducing copyrighted works without author…

cs.CL2024

Token Erasure as a Footprint of Implicit Vocabulary Items in LLMs

Sheridan Feucht, David Atkinson, Byron Wallace +1

LLMs process text as sequences of tokens that roughly correspond to words, where less common words are represented by multiple tokens. However, individual tokens are often semantic…

cs.CL2024★ 1 cited

Locating and Editing Factual Associations in Mamba

Arnab Sen Sharma, David Atkinson, David Bau

We investigate the mechanisms of factual recall in the Mamba state space model. Our work is inspired by previous findings in autoregressive transformer language models suggesting t…

cs.CL2024★ 13 cited

Algorithmic progress in language models

Anson Ho, Tamay Besiroglu, Ege Erdil +6

We investigate the rate at which algorithms for pre-training language models have improved since the advent of deep learning. Using a dataset of over 200 language model evaluations…

cs.AI2023★ 3 cited

Testing Language Model Agents Safely in the Wild

Silen Naihin, David Atkinson, Marc Green +5

A prerequisite for safe autonomy-in-the-wild is safe testing-in-the-wild. Yet real-world autonomous tests face several unique safety challenges, both due to the possibility of caus…