Applied Explainability for Large Language Models: A Comparative Study

2026-04-20 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, quick

Summary

A study submitted on April 15, 2026, by Venkata Abhinandan Kancharla, presents an applied comparative analysis of three explainability techniques for Large Language Models (LLMs): Integrated Gradients, Attention Rollout, and SHAP. The research evaluates these methods on a fine-tuned DistilBERT model performing SST-2 sentiment classification, focusing on their practical behavior in a consistent and reproducible setup rather than proposing new techniques. Findings indicate that gradient-based attribution offers more stable and intuitive explanations, while attention-based methods are computationally efficient but less aligned with prediction-relevant features. Model-agnostic approaches, though flexible, incur higher computational costs and variability. The work emphasizes that these methods serve as diagnostic tools for transformer-based NLP systems, highlighting key trade-offs.

Key takeaway

For AI Engineers and Research Scientists evaluating LLM explainability, prioritize gradient-based attribution methods like Integrated Gradients for more stable and intuitive insights into model decisions. While attention-based methods offer computational efficiency, they may not always align with the most prediction-relevant features. Factor in the higher computational cost and variability of model-agnostic approaches like SHAP when designing your diagnostic workflows for transformer-based NLP systems.

Key insights

Gradient-based explainability provides stable LLM insights, while attention is efficient but less aligned with predictions.

Principles

• Explainability methods are diagnostic tools.
• Trade-offs exist between stability, efficiency, and alignment.

Method

The study compared Integrated Gradients, Attention Rollout, and SHAP on a fine-tuned DistilBERT model for SST-2 sentiment classification to evaluate practical behavior of existing explainability techniques.

In practice

• Use gradient-based methods for stable explanations.
• Consider attention-based methods for computational efficiency.

Topics

• Large Language Models
• Model Explainability
• Integrated Gradients
• Attention Rollout
• SHAP

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.