sp26-cs639 lecturepromptingin-context-learningchain-of-thoughtcs639
Lecture 14: Using FMs: Prompting, ICL Lecture 14: Using FMs: Prompting, ICL
Content locked
The catalog is public. Reading this content requires approved access.
Sign in to readLecture 14: Using FMs — Prompting & In-Context Learning
Review: Other Modalities and Domains
Audio
Audio + Video + Text
Foundation Models in Robotics
SayCan
Basics of Prompting
Prompting: Ask Your Model
Zero-shot vs Few-shot
Few-shot vs. In-context Learning
Few-Shot Choices
Prompt Formats (Historical)
Prompt Formats: Instruction-Based
Choice of Examples
Order of Examples
Improving and Extending Prompting
Hard Prompting
Discrete Optimization
Soft Prompting
Prefix-Tuning
Ensembling Prompts
Weighted Version
LLMs as Optimizers: Prompt Optimization
More Auto-Prompting Work
Promptbreeder
GEPA
Prompting VLMs and Multimodal Models
How to Prompt VLMs?
LLMs to Improve VLMs: Description
LLMs to Improve VLMs: Spurious Features
In-Context Learning for VLMs
Chain-of-Thought
Applications
Zero-Shot CoT
Few-Shot Examples for CoT
Generalizations: Tree of Thoughts