Summary of A Comprehensive Guide to Explainable Ai: From Classical Models to Llms, by Weiche Hsieh et al.

A Comprehensive Guide to Explainable AI: From Classical Models to LLMs

by Weiche Hsieh, Ziqian Bi, Chuanqi Jiang, Junyu Liu, Benji Peng, Sen Zhang, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Keyu Chen, Pohsun Feng, Yizhu Wen, Xinyuan Song, Tianyang Wang, Ming Liu, Junjie Yang, Ming Li, Bowen Jing, Jintao Ren, Junhao Song, Hong-Ming Tseng, Yichao Zhang, Lawrence K.Q. Yan, Qian Niu, Silin Chen, Yunze Wang, Chia Xin Liang

First submitted to arxiv on: 1 Dec 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This comprehensive guide to Explainable Artificial Intelligence (XAI) bridges foundational concepts with advanced methodologies. The book explores interpretability in traditional models like Decision Trees, Linear Regression, and Support Vector Machines. It also tackles the challenges of explaining deep learning architectures like CNNs, RNNs, and Large Language Models (LLMs), including BERT, GPT, and T5. Practical techniques presented include SHAP, LIME, Grad-CAM, counterfactual explanations, and causal inference, supported by Python code examples for real-world applications.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This book is about making artificial intelligence more transparent and understandable. It shows how to explain decisions made by AI systems, so people can trust them. The book covers traditional models like decision trees and simple machines. It also explains complex deep learning models like CNNs and LLMs (which include BERT). To make it easier to understand, the book provides code examples in Python for real-world applications.