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Related Papers

GlobEnc: Quantifying Global Token Attribution by Incorporating the Whole Encoder Layer in Transformers

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractThere 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 …...

21515

Quantifying Context Mixing in Transformers

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractSelf-attention weights and their transformed variants have been the main source of information for analyzing token-to-token interactions in Transformer-based models. But despite their ease of interpretation, these weights are not …...

42769