From decision trees to LLMs - tracking the evolution of AI explainability across two decades.
This paper provides a comprehensive historical analysis of Multimodal Explainable AI (MXAI) across four major eras, offering insights into making complex AI systems more transparent and interpretable.
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https://arxiv.org/abs/2412.14056
🤔 Original Problem:
→ As AI systems become more complex and handle multiple data types, traditional explainability methods fall short in providing clear interpretations of model decisions.
→ The rise of LLMs has further complicated this challenge by introducing new layers of complexity in processing multimodal data.
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🔍 Solution in this Paper:
→ The paper categorizes MXAI development across four distinct eras: traditional machine learning (2000-2009), deep learning (2010-2016), discriminative foundation models (2017-2021), and generative LLMs (2022-2024).
→ For each era, it analyzes three key aspects: data explainability, model explainability, and post-hoc explainability.
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💡 Key Insights:
→ Traditional machine learning era focused on interpretable models like decision trees and basic visualization tools
→ Deep learning era introduced neural networks for processing multimodal data
→ Discriminative foundation models era brought attention-based interpretability
→ Generative LLMs era focuses on interactive and adaptive explanations
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