Description:
Learn about advanced concepts in AI uncertainty quantification and prompting techniques in this comprehensive lecture. Explore temperature scaling methods and Bayesian approaches to calibration before diving into free-text explanations and chain-of-thought prompting. Master in-context learning (ICL) principles and their reliable implementation, while understanding prompt-based fine-tuning strategies. Examine practical applications through case studies of FLAN-T5 and LLaMA Chat models. Gain insights into how these techniques improve AI model performance and reliability through detailed explanations and real-world examples.
Uncertainty, Prompting, and Chain-of-Thoughts in Large Language Models - Part 2
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#Computer Science
#Artificial Intelligence
#Natural Language Processing (NLP)
#Prompt Engineering
#Machine Learning
#LLM (Large Language Model)
#LLaMA (Large Language Model Meta AI)
#Mathematics
#Statistics & Probability
#Uncertainty Quantification
#Language Models
#In-context Learning
#Flan-T5