- Three Emergent Abilities for LLMs are In-Context Learning (ICL), Instruction Following & Step-By-Step Reasoning (CoT).
- Key Practices related to LLMs are: Scaling, Training, Ability Eliciting, Alignment Tuning, Tools.
- Adaption Of LLMs include: Instruction Tuning, Alignment Tuning, Memory-Efficient Model Adaptation, etc.
- Prompt Engineering: In-Context Learning, Chain-Of-Thought, Planning.
- ICL Prompt Engineering Implementations: KATE, EPR, SG-ICL, APE, Structured Prompting, GlobalE & LocalE.
- CoT Prompt Engineering Implementations: Complex CoT, Auto-CoT, Selection-Inference, Self-consistency, DIVERSE, Rationale-augmented ensembles.
- Planning Prompt Engineering Implementations: Least-to-most prompting, DECOMP, PS, Faithful CoT, PAL, HuggingGPT, AdaPlanner, TIP, RAP, ChatCoT, ReAct, Reflexion, Tree of Thoughts.
- Key Practices related to LLMs are: Scaling, Training, Ability Eliciting, Alignment Tuning, Tools.
- Adaption Of LLMs include: Instruction Tuning, Alignment Tuning, Memory-Efficient Model Adaptation, etc.
- Prompt Engineering: In-Context Learning, Chain-Of-Thought, Planning.
- ICL Prompt Engineering Implementations: KATE, EPR, SG-ICL, APE, Structured Prompting, GlobalE & LocalE.
- CoT Prompt Engineering Implementations: Complex CoT, Auto-CoT, Selection-Inference, Self-consistency, DIVERSE, Rationale-augmented ensembles.
- Planning Prompt Engineering Implementations: Least-to-most prompting, DECOMP, PS, Faithful CoT, PAL, HuggingGPT, AdaPlanner, TIP, RAP, ChatCoT, ReAct, Reflexion, Tree of Thoughts.
The authors map the landscape of frameworks for abstracting interactions with and between large language models, and suggest two systems of organization for reasoning about the various approaches to, and philosophies of, LLM abstraction.
free and paid courses available for learning about Artificial Intelligence (AI). It mentions courses from NVIDIA, Google Cloud Skills Boost program, Amazon's training center, OpenAI's partner DeepLearning.ai, edX, and Microsoft.
• A beginner's guide to understanding Hugging Face Transformers, a library that provides access to thousands of pre-trained transformer models for natural language processing, computer vision, and more.
• The guide covers the basics of Hugging Face Transformers, including what it is, how it works, and how to use it with a simple example of running Microsoft's Phi-2 LLM in a notebook
• The guide is designed for non-technical individuals who want to understand open-source machine learning without prior knowledge of Python or machine learning.
• The guide covers the basics of Hugging Face Transformers, including what it is, how it works, and how to use it with a simple example of running Microsoft's Phi-2 LLM in a notebook
• The guide is designed for non-technical individuals who want to understand open-source machine learning without prior knowledge of Python or machine learning.
