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Awesome GitHub RepositoriesLanguage Model Interpretability
Specialized techniques for explaining the outputs of large-scale natural language models.
Distinct from Large Language Models: Focuses on explaining LLM outputs rather than orchestrating or deploying them
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Awesome Language Model Interpretability GitHub Repositories
Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
- slundberg/shap
slundberg/shap
25,535Vezi pe GitHubSHAP is a machine learning explainer that uses a game-theoretic framework to estimate the contribution of each feature to a model prediction. It provides a set of tools for quantifying how individual input features push a specific output away from a baseline value. The project includes specialized explainers for different architectures, including high-speed implementations for decision trees and ensemble models, linearization algorithms for deep learning networks, and covariance integration for linear models. It also features a model-agnostic interpretability tool that uses a kernel method to
Uses coalitional rules to explain the outputs of large natural language models with reduced function evaluations.
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