Introduction Transformers have revolutionized AI with their performance, yet they remain black boxes – complex webs of attention and activation that defy easy explanation (Layer-Wise Sub-Model Interpretability for Transformers | artsen). Earlier in Layer-Wise Sub-Model Interpretability for Transformers, we explored breaking a transformer into interpretable layer-wise sub-models, treating each layer’s hidden state as an input to an explanatory module (Layer-Wise Sub-Model Interpretability for Transformers | artsen) … Read More

In recent years, researchers have developed numerous methods to peer inside transformer models and understand how they work. Building on the concept of layer-wise sub-model interpretability – treating each layer or component as an interpretable sub-model – this report delves into advanced techniques that enhance model transparency. We examine theoretical foundations, such as saliency maps, attention analysis, causal interventions, neuron-level studies, mechanistic interpretability (circuits), and probing. … Read More

A Novel Approach to Improving AI Interpretability 1. Introduction Transformers have achieved remarkable success in NLP and other domains, but they operate as complex black-box models, making their decisions hard to interpret (A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models). This lack of transparency has raised concerns about safety, trust, and accountability when deploying AI systems in real-world applications (A Practical Review of Mechanistic … Read More