1 citations · 1 across the 4 of their papers we have counts for
7 papers
AlignTree: Efficient Defense Against LLM Jailbreak Attacks
Gil Goren, Shahar Katz, Lior Wolf
Large Language Models (LLMs) are vulnerable to adversarial attacks that bypass safety guidelines and generate harmful content. Mitigating these vulnerabilities requires defense mec…
Detecting and Pruning Prominent but Detrimental Neurons in Large Language Models
Ameen Ali, Shahar Katz, Lior Wolf +1
Large language models (LLMs) often develop learned mechanisms specialized to specific datasets, such as reliance on domain-specific correlations, which yield high-confidence predic…
Execution Guided Line-by-Line Code Generation
Boaz Lavon, Shahar Katz, Lior Wolf
We present a novel approach to neural code generation that incorporates real-time execution signals into the language model generation process. While large language models (LLMs) h…
ConsiStyle: Style Diversity in Training-Free Consistent T2I Generation
Yohai Mazuz, Janna Bruner, Lior Wolf
In text-to-image models, consistent character generation is the task of achieving text alignment while maintaining the subject's appearance across different prompts. However, since…
IlluSign: Illustrating Sign Language Videos by Leveraging the Attention Mechanism
Janna Bruner, Amit Moryossef, Lior Wolf
Sign languages are dynamic visual languages that involve hand gestures, in combination with non manual elements such as facial expressions. While video recordings of sign language…
Segment-Based Attention Masking for GPTs
Shahar Katz, Liran Ringel, Yaniv Romano +1
Modern Language Models (LMs) owe much of their success to masked causal attention, the backbone of Generative Pre-Trained Transformer (GPT) models. Although GPTs can process the en…