CoT Prompting can guide LLMs through a logical reasoning chain
E.g. show reasoning process and final answer for multi-step math word problem and mimic how humans break down problems into logical intermediate steps
There’s also Contrastive Chain of Thought prompting, which seeks to learn from mistakes, but is noted as limited in literature
Automatic Chain of Thought Prompting
Enhancement of above, improves few-shot learning
Self-Consistency
Decoding strategy
Generates diverse reasoning chains by sampling from language model’s decoder for complex reasoning tasks with multiple valid paths
Graph-of-Thought Prompting
Framework permits dynamic interplay, backtracking, and evaluation of ideas
System 2 Attention Prompting
Uses reasoning abilities of LLMs by employing a two-step process to enhance attention and response quality by employing context regeneration and response generation with refined context
Not sure why, but it makes me think of when I will try to run something back after a meeting to make sure I understand
Actually this might be called Rephrase and Respond Prompting
Reduce Hallucination
Retrieval Augmented Generation
Analyzes user input, crafts a targeted query, and scours a pre-built knowledge base for relevant resources
Retrieved snippets are incorporated into original primpt
Should allow LLM to generate creative, factually accurate responses
Reason and Act (ReAct) Prompting
Concurrently generates reasoning traces and task-specific actions
Can handle multi-language
Can use Wikipedia API to help control hallucination and error propagation
Chain-of-Note Prompting
Can handle noisy, irrelevant documents and address unknown scenarios
Chain-of-Knowledge Prompting
Tries to break down tasks into well-coordinated steps
Reasoning preparation stage
Dynamic knowledge adaption phase
Knowledge-Based Reasoning and Generation
Automatic Reasoning and Tool-Use
Metacognition and Self-Reflection
Take a Step Back Prompting
Extract high-level concepts
Wonder if this can be used to get “difficulties” or as an alternate for topic modeling/clustering