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cs.CL2024★ 1 cited
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…
cs.CL2024
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…
cs.CL2024★ 1 cited
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…