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.
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