3 citations · 6 across the 11 of their papers we have counts for
11 papers
Human-Provenance Verification should be Treated as Labor Infrastructure in AI-Saturated Markets
Erin McGurk, David Khachaturov
We argue that AI-saturated markets are likely to create Veblen-good premiums, which we term human-provenance premiums, for verified human presence, and hence AI governance should t…
Controllable and explainable personality sliders for LLMs at inference time
Florian Hoppe, David Khachaturov, Robert Mullins +1
Aligning Large Language Models (LLMs) with specific personas typically relies on expensive and monolithic Supervised Fine-Tuning (SFT) or RLHF. While effective, these methods requi…
Tiered Anonymity on Social-Media Platforms as a Countermeasure against Deepfakes and LLM-Driven Mass Misinformation
David Khachaturov, Roxanne Schnyder, Robert Mullins
We argue that governments should mandate a three-tier anonymity framework on social-media platforms as a reactionary measure prompted by the ease-of-production of deepfakes and lar…
Adversarial Suffix Filtering: a Defense Pipeline for LLMs
David Khachaturov, Robert Mullins
Large Language Models (LLMs) are increasingly embedded in autonomous systems and public-facing environments, yet they remain susceptible to jailbreak vulnerabilities that may under…
Watermarking Needs Input Repetition Masking
David Khachaturov, Robert Mullins, Ilia Shumailov +1
Recent advancements in Large Language Models (LLMs) raised concerns over potential misuse, such as for spreading misinformation. In response two counter measures emerged: machine l…
"I am bad": Interpreting Stealthy, Universal and Robust Audio Jailbreaks in Audio-Language Models
Isha Gupta, David Khachaturov, Robert Mullins
The rise of multimodal large language models has introduced innovative human-machine interaction paradigms but also significant challenges in machine learning safety. Audio-Languag…
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness
David Khachaturov, Robert Mullins
Quantifying robustness in a single measure for the purposes of model selection, development of adversarial training methods, and anticipating trends has so far been elusive. The si…
Scaling Trends for Data Poisoning in LLMs
Dillon Bowen, Brendan Murphy, Will Cai +3
LLMs produce harmful and undesirable behavior when trained on datasets containing even a small fraction of poisoned data. We demonstrate that GPT models remain vulnerable to fine-t…
QBI: Quantile-Based Bias Initialization for Efficient Private Data Reconstruction in Federated Learning
Micha V. Nowak, Tim P. Bott, David Khachaturov +3
Federated learning enables the training of machine learning models on distributed data without compromising user privacy, as data remains on personal devices and only model updates…
Human-Producible Adversarial Examples
David Khachaturov, Yue Gao, Ilia Shumailov +3
Visual adversarial examples have so far been restricted to pixel-level image manipulations in the digital world, or have required sophisticated equipment such as 2D or 3D printers…
Markpainting: Adversarial Machine Learning meets Inpainting
David Khachaturov, Ilia Shumailov, Yiren Zhao +2
Inpainting is a learned interpolation technique that is based on generative modeling and used to populate masked or missing pieces in an image; it has wide applications in picture…