6 papers
REMIND: Input Loss Landscapes Reveal Residual Memorization in Post-Unlearning LLMs
Liran Cohen, Yaniv Nemcovesky, Avi Mendelson
Machine unlearning aims to remove the influence of specific training data from a model without requiring full retraining. This capability is crucial for ensuring privacy, safety, a…
Silent Tokens, Loud Effects: Padding in LLMs
Rom Himelstein, Amit LeVi, Yonatan Belinkov +1
Padding tokens are widely used in large language models (LLMs) to equalize sequence lengths during batched inference. While they should be fully masked, implementation errors can c…
Representing LLMs in Prompt Semantic Task Space
Idan Kashani, Avi Mendelson, Yaniv Nemcovsky
Large language models (LLMs) achieve impressive results over various tasks, and ever-expanding public repositories contain an abundance of pre-trained models. Therefore, identifyin…
: Reconstruction, Raw, and Rain: Deraining Directly in the Bayer Domain
Nate Rothschild, Moshe Kimhi, Avi Mendelson +1
Image reconstruction from corrupted images is crucial across many domains. Most reconstruction networks are trained on post-ISP sRGB images, even though the image-signal-processing…
Sparse patches adversarial attacks via extrapolating point-wise information
Yaniv Nemcovsky, Avi Mendelson, Chaim Baskin
Sparse and patch adversarial attacks were previously shown to be applicable in realistic settings and are considered a security risk to autonomous systems. Sparse adversarial pertu…
Hysteresis Activation Function for Efficient Inference
Moshe Kimhi, Idan Kashani, Avi Mendelson +1
The widely used ReLU is favored for its hardware efficiency, {as the implementation at inference is a one bit sign case,} yet suffers from issues such as the ``dying ReLU'' problem…