Probing Information Distribution in Transformer Architectures through Entropy Analysis
This work addresses the interpretability and evaluation of Transformer-based models, which is an incremental contribution to the field of machine learning.
The paper tackled the problem of understanding information distribution in Transformer architectures by using entropy analysis to quantify token-level uncertainty and examine entropy patterns across processing stages, applied to a GPT-based model to reveal insights into model behavior and internal representations.
This work explores entropy analysis as a tool for probing information distribution within Transformer-based architectures. By quantifying token-level uncertainty and examining entropy patterns across different stages of processing, we aim to investigate how information is managed and transformed within these models. As a case study, we apply the methodology to a GPT-based large language model, illustrating its potential to reveal insights into model behavior and internal representations. This approach may offer insights into model behavior and contribute to the development of interpretability and evaluation frameworks for transformer-based models