Probing Information Distribution in Transformer Architectures through Entropy Analysis

TL;DR

This paper introduces entropy analysis as a method to investigate how information is distributed and transformed within Transformer architectures, using a GPT model as a case study to enhance interpretability.

Contribution

It presents a novel approach to analyze token-level uncertainty and entropy patterns in Transformers, aiding understanding of internal representations and model behavior.

Findings

Entropy patterns reveal information flow within the model

Token uncertainty varies across processing stages

Method enhances interpretability of Transformer models

Abstract

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