Abstract
We propose a new method to analyze Transformer language models. In Transformer self-attention modules, attention weights are calculated from the query vectors and key vectors. Then, output vectors are obtained by taking the weighted sum of value vectors. While existing works on analysis of Transformer have focused on attention weights, this work focused on value and output matrices. We obtain joint matrices by multiplying both matrices, and show that the trace of the joint matrices are correlated with word co-occurences.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Transactions of the Japanese Society for Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.