Dillon Bowen

According to our database, Dillon Bowen authored at least 10 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Auditing Games for Sandbagging.

Jordan Taylor, Sid Black, Dillon Bowen, Thomas Read, Satvik Golechha, Alex Zelenka-Martin, Oliver Makins, Connor Kissane, Kola Ayonrinde, Jacob Merizian, Samuel Marks, Chris Cundy, Joseph Isaac Bloom

CoRR, December, 2025

Jailbreak-Tuning: Models Efficiently Learn Jailbreak Susceptibility.

Brendan Murphy, Dillon Bowen, Shahrad Mohammadzadeh, Julius Broomfield, Adam Gleave, Kellin Pelrine

CoRR, July, 2025

The Safety Gap Toolkit: Evaluating Hidden Dangers of Open-Source Models.

Ann-Kathrin Dombrowski, Dillon Bowen, Adam Gleave, Chris Cundy

CoRR, July, 2025

AI Companies Should Report Pre- and Post-Mitigation Safety Evaluations.

Dillon Bowen, Ann-Kathrin Dombrowski, Adam Gleave, Chris Cundy

CoRR, March, 2025

Jailbreak-Tuning: Models Efficiently Learn Jailbreak Susceptibility.

Brendan Murphy, Dillon Bowen, Shahrad Mohammadzadeh, Tom Tseng, Julius Broomfield, Adam Gleave, Kellin Pelrine

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

Scaling Trends for Data Poisoning in LLMs.

Dillon Bowen, Brendan Murphy, Will Cai, David Khachaturov, Adam Gleave, Kellin Pelrine

Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024

Scaling Laws for Data Poisoning in LLMs.

Dillon Bowen, Brendan Murphy, Will Cai, David Khachaturov, Adam Gleave, Kellin Pelrine

CoRR, 2024

A StrongREJECT for Empty Jailbreaks.

Alexandra Souly, Qingyuan Lu, Dillon Bowen, Tu Trinh, Elvis Hsieh, Sana Pandey, Pieter Abbeel, Justin Svegliato, Scott Emmons, Olivia Watkins, Sam Toyer

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2022

Multiple Inference: A Python package for comparing multiple parameters.

Dillon Bowen

J. Open Source Softw., 2022

2020

Generalized SHAP: Generating multiple types of explanations in machine learning.

Dillon Bowen, Lyle H. Ungar

CoRR, 2020