1 citations · 1 across the 2 of their papers we have counts for
11 papers
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
Yuval Reif, Roy Schwartz
Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However,…
ARIA: Adversarially Robust Image Attribution for Content Provenance
Maksym Andriushchenko, Xiaoyang Rebecca Li, Geoffrey Oxholm +4
Image attribution -- matching an image back to a trusted source -- is an emerging tool in the fight against online misinformation. Deep visual fingerprinting models have recently b…
RobustBench: a standardized adversarial robustness benchmark
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag +5
As a research community, we are still lacking a systematic understanding of the progress on adversarial robustness which often makes it hard to identify the most promising ideas in…
Understanding and Improving Fast Adversarial Training
Maksym Andriushchenko, Nicolas Flammarion
A recent line of work focused on making adversarial training computationally efficient for deep learning models. In particular, Wong et al. (2020) showed that -adversa…
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines
Marius Mosbach, Maksym Andriushchenko, Dietrich Klakow
Fine-tuning pre-trained transformer-based language models such as BERT has become a common practice dominating leaderboards across various NLP benchmarks. Despite the strong empiri…
Square Attack: a query-efficient black-box adversarial attack via random search
Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion +1
We propose the Square Attack, a score-based black-box - and -adversarial attack that does not rely on local gradient information and thus is not affected by gradient…