Self-Consistency & Generated Knowledge
Implement techniques where LLMs generate multiple reasoning paths and choose the most consistent answer, or generate knowledge to aid reasoning.
Intro: Consistency & Knowledge
Welcome to Lesson 2! In complex problem-solving, Large Language Models (LLMs) can sometimes struggle, leading to incorrect or inconsistent answers.
This lesson introduces two powerful techniques to boost their reliability: Self-Consistency and Generated Knowledge. These methods help LLMs 'think' more deeply and systematically.
Why Advanced Reasoning?
LLMs are great at generating text, but they can sometimes make logical errors or 'hallucinate' (produce factually incorrect information), especially with multi-step reasoning.
Advanced prompting strategies like Self-Consistency and Generated Knowledge aim to:
- Improve accuracy for complex tasks.
- Reduce the likelihood of factual errors.
- Make LLM responses more robust and reliable.
All lessons in this course
- Chain-of-Thought Prompting
- Self-Consistency & Generated Knowledge
- Tree-of-Thought & Graph Prompts
- ReAct: Reasoning and Acting with Tools