Classical Attention Analysis in Neural Networks

Description

Classical attention mechanisms in RNNs and CNNs create alignment matrices and temporal attention patterns that show how models focus on different input elements over time or space. This technique analyses these traditional attention patterns, particularly in encoder-decoder architectures and sequence-to-sequence models, where attention weights reveal which source elements influence each output step. Unlike transformer self-attention analysis, this focuses on understanding alignment patterns, temporal dependencies, and encoder-decoder attention dynamics in classical neural architectures.

Example Use Cases

Explainability

Analysing encoder-decoder attention in a neural machine translation model to verify the alignment between source and target words, ensuring the model learns proper translation correspondences rather than positional biases.

Examining temporal attention patterns in an RNN-based image captioning model to understand how attention moves across different image regions as it generates each word of the caption description.

Limitations

Attention weights are not always strongly correlated with feature importance for the final prediction.
High attention does not necessarily imply causal influence - models can attend to irrelevant but correlated features.
Only applicable to neural network architectures that explicitly use attention mechanisms.
Interpretation can be misleading without understanding the specific attention mechanism implementation and training dynamics.

Resources

An Attentive Survey of Attention Models

Documentation•S. Chaudhari et al.

Attention, please! A survey of neural attention models in deep learning

Documentation•Alana de Santana Correia and E. Colombini

ecco - Explain, Analyze, and Visualize NLP Language Models

Software Package

Enhancing Sentiment Analysis of Twitter Data Using Recurrent Neural Networks with Attention Mechanism

Research Paper•S. Nithya et al.

Can Neural Networks Develop Attention? Google Thinks they Can ...

Tutorial

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Causal Mediation Analysis in Language Models	Causal mediation analysis in language models is a mechanistic interpretability technique that systematically investigates how specific internal components (neurons, attention heads, or layers) causally contribute to model outputs. By performing controlled interventions—such as activating, deactivating, or modifying specific components—researchers can trace the causal pathways through which information flows and transforms within the model. This approach goes beyond correlation to establish causal relationships, enabling researchers to understand not just what features influence outputs, but how and why they do so through specific computational pathways.	Explainability Reliability Safety
Attention Visualisation in Transformers	Attention Visualisation in Transformers analyses the multi-head self-attention mechanisms that enable transformers to process sequences by attending to different positions simultaneously. The technique visualises attention weights as heatmaps showing how strongly each token attends to every other token across different heads and layers. By examining these attention patterns, practitioners can understand how models like BERT, GPT, and T5 build contextual representations, identify which tokens influence predictions most strongly, and detect potential biases in how the model processes different types of input. This provides insights into positional encoding effects, head specialisation patterns, and the evolution of attention from local to global context across layers.	Explainability Fairness Transparency
RuleFit	RuleFit is a method that creates an interpretable model by combining linear terms with decision rules. It first extracts potential rules from ensemble trees, then builds a sparse linear model where those rules (binary conditions) and original features are used as predictors, with regularization to keep the model simple. The final model is a linear combination of a small set of rules and original features, balancing interpretability with predictive power.	Explainability Transparency