1 citations · 2 across the 9 of their papers we have counts for
10 papers
ClusterFewshot: Improving Few-shot Optimization for LLMs workflow
Omri Bar Haim, Shahar Katz, Lior Wolf
The performance of large language model (LLM) workflows often depends on selecting a small set of in-context demonstrations to guide model behavior on new tasks. Recent methods imp…
Mean-Field Parallel Decoding for Discrete Diffusion Language Models
Tamim Zoabi, Ameen Ali, Liran Ringel +1
Discrete diffusion language models enable parallel token generation, offering a pathway to low-latency decoding. However, selecting tokens independently by marginal confidence limi…
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…
Reversed Attention: On The Gradient Descent Of Attention Layers In GPT
Shahar Katz, Lior Wolf
The success of Transformer-based Language Models (LMs) stems from their attention mechanism. While this mechanism has been extensively studied in explainability research, particula…
Backward Lens: Projecting Language Model Gradients into the Vocabulary Space
Shahar Katz, Yonatan Belinkov, Mor Geva +1
Understanding how Transformer-based Language Models (LMs) learn and recall information is a key goal of the deep learning community. Recent interpretability methods project weights…