15 citations · 20 across the 9 of their papers we have counts for
4 papers · 1 filter
Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
Michal Moshkovitz, Suraj Srinivas, Lesia Semenova +7
Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows. In practice,…
EMA-Nesterov: Stabilizing Nesterov's Lookahead for Accelerated Deep Learning Optimization
Chung-Yiu Yau, Dawei Li, Athanasios Glentis +3
Lookahead-based acceleration methods, such as Nesterov's momentum, are widely used in optimization, but they often become unreliable in deep learning training mainly due to stochas…
An Interpretable N-gram Perplexity Threat Model for Large Language Model Jailbreaks
Valentyn Boreiko, Alexander Panfilov, Vaclav Voracek +2
A plethora of jailbreaking attacks have been proposed to obtain harmful responses from safety-tuned LLMs. These methods largely succeed in coercing the target output in their origi…
How Much Can We Forget about Data Contamination?
Sebastian Bordt, Suraj Srinivas, Valentyn Boreiko +1
The leakage of benchmark data into the training data has emerged as a significant challenge for evaluating the capabilities of large language models (LLMs). In this work, we challe…