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Visualization and analysis of generated text candidates, including probability-based diffs against reference texts.
Distinct from Sequence Generation: Focuses on the debugging and comparative analysis of generated sequences rather than the generation process itself
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- pair-code/lit
PAIR-code/lit
3,636Auf GitHub ansehenLit is a machine learning interpretability framework and model debugging tool designed to analyze model behavior and performance. It serves as an interpretability dashboard for large language models and a general performance analyzer for text, image, and tabular datasets. The project distinguishes itself through a comprehensive suite of interpretability tools, including salience map generation for feature attribution, the creation of synthetic and counterfactual examples to test robustness, and the projection of high-dimensional embeddings into visual spaces via UMAP or PCA. It further enable
Visualizes generated text candidates and highlights diffs against reference texts using output probabilities.
TypeScriptmachine-learningnatural-language-processingvisualization
