3 citations · 5 across the 8 of their papers we have counts for
9 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…
TensorLens: End-to-End Transformer Analysis via High-Order Attention Tensors
Ido Andrew Atad, Itamar Zimerman, Shahar Katz +1
Attention matrices are fundamental to transformer research, supporting a broad range of applications including interpretability, visualization, manipulation, and distillation. Yet,…
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…
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…
VISIT: Visualizing and Interpreting the Semantic Information Flow of Transformers
Shahar Katz, Yonatan Belinkov
Recent advances in interpretability suggest we can project weights and hidden states of transformer-based language models (LMs) to their vocabulary, a transformation that makes the…