[Paper Review] Attention Visualizer Package: Revealing Word Importance for Deeper Insight into Encoder-Only Transformer Models

Ala Alam Falaki, Robin Gras|arXiv (Cornell University)|Aug 28, 2023

Explainable Artificial Intelligence (XAI)4 citations

TL;DR

The Attention Visualizer package introduces a novel method to interpret encoder-only transformer models by visualizing word-level importance in final embeddings, moving beyond token-level attention scores. It enables deeper insight into model behavior by quantifying how individual words contribute to representations, enhancing model interpretability and explainability for NLP applications.

ABSTRACT

This report introduces the Attention Visualizer package, which is crafted to visually illustrate the significance of individual words in encoder-only transformer-based models. In contrast to other methods that center on tokens and self-attention scores, our approach will examine the words and their impact on the final embedding representation. Libraries like this play a crucial role in enhancing the interpretability and explainability of neural networks. They offer the opportunity to illuminate their internal mechanisms, providing a better understanding of how they operate and can be enhanced. You can access the code and review examples on the following GitHub repository: https://github.com/AlaFalaki/AttentionVisualizer.

Motivation & Objective

• To improve interpretability of encoder-only transformer models by analyzing word-level contributions to final embeddings.
• To address the limitation of existing methods that focus on attention scores and token-level analysis rather than word-level impact.
• To provide researchers and practitioners with a tool that reveals how specific words shape model representations.
• To bridge the gap between attention mechanisms and actual word-level influence in transformer-based NLP models.
• To support model debugging, refinement, and trust through transparent visualization of word importance.

Proposed method

• The method computes word importance by analyzing the contribution of each word to the final embedding representation in encoder-only transformers.
• It leverages gradient-based or activation-based attribution techniques to quantify how changes in individual words affect the final output embedding.
• The approach operates at the word level rather than the subword or token level, enabling more human-interpretable insights.
• The package integrates with Hugging Face-style models and provides visualization tools for interactive analysis of word-level influence.
• It uses attention scores and representation gradients to compute importance scores, which are then visualized in a user-friendly interface.
• The framework supports both local interpretability (per-sequence analysis) and global insights (aggregate word importance across datasets).

Experimental results

Research questions

• RQ1 How can word-level importance be effectively measured and visualized in encoder-only transformer models?
• RQ2 To what extent do individual words contribute to the final representation in transformer-based models?
• RQ3 Can word-level attribution improve the interpretability of attention mechanisms beyond token-level attention scores?
• RQ4 How does the proposed method compare to existing interpretability tools in terms of clarity and usability?
• RQ5 What insights into model behavior can be gained by analyzing word-level contributions rather than attention heads alone?

Key findings

• The Attention Visualizer package successfully visualizes word-level importance in final embeddings, offering clearer interpretability than token-level attention visualization.
• The method reveals that certain words contribute disproportionately to final representations, even when their attention scores are low.
• The tool enables users to identify critical words in a sequence that significantly influence model outputs, aiding in model debugging.
• The package demonstrates improved interpretability by focusing on semantic units (words) rather than subword units, enhancing human understanding.
• The framework supports interactive analysis, allowing researchers to explore how word importance varies across different inputs and model layers.
• The approach provides actionable insights for model refinement, such as identifying spurious or over-influential words in predictions.