GlobEnc: Quantifying Global Token Attribution by Incorporating the Whole Encoder Layer in Transformers (2022.naacl-main)
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| Challenge: | Existing methods for interpreting the underlying dynamics of Transformers have been criticized for their lack of reliability. |
| Approach: | They propose a token attribution analysis method that incorporates all components in the encoder block and aggregates this across layers. |
| Outcome: | The proposed method significantly outperforms existing methods on saliency scores and correlation with gradient-based salience scores. |
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| Challenge: | Using simple linear transformations, Transformer encoders can be sped up with limited accuracy costs by replacing the self-attention sublayers with simple linear mixing mechanisms. |
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LAIT: Efficient Multi-Segment Encoding in Transformers with Layer-Adjustable Interaction (2023.acl-long)
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Jeremiah Milbauer, Annie Louis, Mohammad Javad Hosseini, Alex Fabrikant, Donald Metzler, Tal Schuster
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