Publisher's Synopsis
It's more important than ever to understand how powerful AI systems think in a world where these systems are becoming more and more prevalent. Interpreting Large Language Models is your go-to resource for comprehending the "black box" that drives contemporary AI, from ChatGPT to BERT and beyond.
Whether you're a technical leader, data scientist, machine learning engineer, or just interested in the inner workings of language models, this book demystifies transformers' inner workings and demonstrates how to confidently interpret, display, and audit their judgments. You will learn how to:- Decode attention maps to see what the model is "focusing" on
- Use saliency, gradients, and attribution tools like SHAP and LIME to trace the origins of AI decisions
- Through practical examples, visual walkthroughs, and hands-on techniques#Investigate embedding spaces with UMAP and t-SNE to find biases and hidden patterns.
- Recognize delusions and unfair results-and create more moral, comprehensible systems
- Explainability strategies designed for practical applications can help you get ready for audits, compliance, and regulatory scrutiny.
Beyond technical schematics, this book explores the philosophy of explainable AI with clarity and a human-first approach: Why is it necessary? For whom is it intended? And how may it assist us in creating trustworthy AI systems? Interpreting Large Language Models is your road map to transparency in the AI era if you're prepared to make the transition from blind trust to informed comprehension.
