The Impact of LoRA on the Emergence of Clusters in Transformers

The Impact of LoRA on the Emergence of Clusters in Transformers

In this paper, we employ the mathematical framework on Transformers developed by \citet{sander2022sinkformers,geshkovski2023emergence,geshkovski2023mathematical} to explore how variations in attention parameters and initial token values impact the structural dynamics of token clusters. Our analysis...

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Veröffentlicht in:arXiv.org 2024-02
Hauptverfasser: Koubbi, Hugo, Boussard, Matthieu, Hernandez, Louis
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Algorithms
Cluster analysis
Dynamic structural analysis
Parameters
Transformers
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Boussard, Matthieu
Hernandez, Louis
description In this paper, we employ the mathematical framework on Transformers developed by \citet{sander2022sinkformers,geshkovski2023emergence,geshkovski2023mathematical} to explore how variations in attention parameters and initial token values impact the structural dynamics of token clusters. Our analysis demonstrates that while the clusters within a modified attention matrix dynamics can exhibit significant divergence from the original over extended periods, they maintain close similarities over shorter intervals, depending on the parameter differences. This work contributes to the fine-tuning field through practical applications to the LoRA algorithm \cite{hu2021lora,peft}, enhancing our understanding of the behavior of LoRA-enhanced Transformer models.
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subjects Algorithms
Cluster analysis
Dynamic structural analysis
Parameters
Transformers
title The Impact of LoRA on the Emergence of Clusters in Transformers
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