用熵分析揭示Transformer模型中信息分布规律
Probing Information Distribution in Transformer Architectures through Entropy Analysis
- 通过计算分词级不确定性，量化信息分布
- 发现模型处理阶段熵值变化模式，反映信息演化过程
- 适用于理解大模型内部机制，适合可解释性研究者
本研究将熵分析作为探查基于Transformer架构中信息分布的工具。通过量化分词级不确定性，并考察不同处理阶段的熵模式，旨在探究信息在模型中的管理与转换方式。以基于GPT的大语言模型为例，该方法展示了揭示模型行为与内部表征的潜力。这一框架有助于深入理解模型运作机制，推动面向Transformer模型的可解释性与评估体系发展。
原文摘要 · Abstract (English)
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
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