Add to ORKGThere has been a growing interest in interpreting the underlying dynamics of Transformers. While self-attention patterns were initially deemed as the primary option, recent studies have shown that integrating other components can yield more accurate explanations. This paper introduces a novel token attribution analysis method that incorporates all the components in the encoder block and aggregates this throughout layers. Through extensive quantitative and qualitative experiments, we demonstrate that our method can produce faithful and meaningful global token attributions. Our experiments reveal that incorporating almost every encoder component results in increasingly more accurate analysis in both local (single layer) and global (the whole ...
In this paper, we aim to build the global convergence theory of encoder-only shallow Transformers un...
Vision Transformers are becoming more and more the preferred solution to many computer vision proble...
In Neural Machine Translation (NMT), each token prediction is conditioned on the source sentence and...
We take a deep look into the behaviour of self-attention heads in the transformer architecture. In l...
The Transformer architecture aggregates input information through the self-attention mechanism, but ...
The Transformer architecture aggregates input information through the self-attention mechanism, but ...
Self-supervised training methods for transformers have demonstrated remarkable performance across va...
We introduce token-consistent stochastic layers in vision transformers, without causing any severe d...
The great success of Transformer-based models benefits from the powerful multi-head self-attention m...
We analyze the Knowledge Neurons framework for the attribution of factual and relational knowledge t...
In this paper, we study the computation of how much an input token in a Transformer model influences...
Image attribution analysis seeks to highlight the feature representations learned by visual models s...
Large pretrained language models using the transformer neural network architecture are becoming a do...
Deep neural networks are very successful on many vision tasks, but hard to interpret due to their bl...
A vast and growing literature on explaining deep learning models has emerged. This paper contributes...
In this paper, we aim to build the global convergence theory of encoder-only shallow Transformers un...
Vision Transformers are becoming more and more the preferred solution to many computer vision proble...
In Neural Machine Translation (NMT), each token prediction is conditioned on the source sentence and...
We take a deep look into the behaviour of self-attention heads in the transformer architecture. In l...
The Transformer architecture aggregates input information through the self-attention mechanism, but ...
The Transformer architecture aggregates input information through the self-attention mechanism, but ...
Self-supervised training methods for transformers have demonstrated remarkable performance across va...
We introduce token-consistent stochastic layers in vision transformers, without causing any severe d...
The great success of Transformer-based models benefits from the powerful multi-head self-attention m...
We analyze the Knowledge Neurons framework for the attribution of factual and relational knowledge t...
In this paper, we study the computation of how much an input token in a Transformer model influences...
Image attribution analysis seeks to highlight the feature representations learned by visual models s...
Large pretrained language models using the transformer neural network architecture are becoming a do...
Deep neural networks are very successful on many vision tasks, but hard to interpret due to their bl...
A vast and growing literature on explaining deep learning models has emerged. This paper contributes...
In this paper, we aim to build the global convergence theory of encoder-only shallow Transformers un...
Vision Transformers are becoming more and more the preferred solution to many computer vision proble...
In Neural Machine Translation (NMT), each token prediction is conditioned on the source sentence and...