Probing Information Distribution in Transformer Architectures through Entropy Analysis

2025-07-21

AI Summary

The internal information distribution mechanism of Transformer models remains poorly understood. Method: This paper proposes an information-entropy-based probing method to quantify token-level uncertainty and model the dynamic entropy evolution across layers, without requiring additional training or labeled supervision. Contribution/Results: Applying systematic entropy trajectory analysis to GPT-family models, we uncover, for the first time, an alternating pattern of information compression and diffusion between feed-forward layers and attention heads, identifying critical information bottlenecks and representation transition pathways. The method offers strong interpretability and establishes the first lightweight, information-flow-oriented analytical framework for Transformer interpretability research. Furthermore, it enables the design of novel model evaluation metrics grounded in entropy dynamics—providing a principled, quantifiable basis for assessing representational efficiency and layer-wise information processing in large language models.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Large Multimodal Models (LMMs)
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

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

Problem

• Analyze information distribution in Transformers via entropy
• Study token-level uncertainty across processing stages
• Develop interpretability frameworks for Transformer models

Innovation

• Using entropy analysis for information distribution
• Quantifying token-level uncertainty in Transformers
• Applying methodology to GPT-based models