Large Language Models are Interpretable Learners

The trade-off between expressiveness and interpretability remains a core challenge when building human-centric predictive models for classification and decision-making. While symbolic rules offer interpretability, they often lack expressiveness, whereas neural networks excel in performance but are k...

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Bibliographische Detailangaben

Hauptverfasser:	Wang, Ruochen, Si, Si, Yu, Felix, Wiesmann, Dorothea, Hsieh, Cho-Jui, Dhillon, Inderjit
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Computer Vision and Pattern Recognition Computer Science - Learning Computer Science - Symbolic Computation
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