gSMILE is a model-agnostic framework designed to provide interpretability for large language models by explaining how specific parts of a prompt influence the generated output. The system functions by making minor variations to input prompts and measuring subsequent changes in responses to identify high-impact words, which are then presented as visual heat maps. This approach aims to demystify black-box systems like GPT, Llama, and Claude for use cases where trust and accountability are essential.
- Model-agnostic interpretability specifically for generative AI solutions.
- Identification of influential tokens through input perturbation.
- Visualization of prompt significance via heat maps.
- Empirical validation using accuracy, consistency, stability, and fidelity metrics.
- Model-agnostic interpretability specifically for generative AI solutions.
- Identification of influential tokens through input perturbation.
- Visualization of prompt significance via heat maps.
- Empirical validation using accuracy, consistency, stability, and fidelity metrics.
This article explains permutation feature importance (PFI), a popular method for understanding feature importance in explainable AI. The author walks through calculating PFI from scratch using Python and XGBoost, discussing the rationale behind the method and its limitations.
