Hey everyone. 👋
With the recent flood of LLMs being announced, it's been a lot of fun experimenting with different methods for prompt engineering. I've been discovering the strenghts and weaknesses of the various models available today. It's quite incredible what these models can achieve with a little creativity and some clever wording,
I thought I'd share some of the techniques I've found effective for getting the best results out of LLMs.
1. Zero-Shot and Few-Shot Prompting
These techniques leverage the model's pre-existing knowledge for tasks with limited or no specific training data.
Key Points:
- Zero-Shot: Relies solely on task description
- Few-Shot: Provides a few input-output examples
- Useful for tasks with scarce labeled data
- Few-shot more effective but requires additional tokens
2. Chain-of-Thought (CoT) Prompting
Enhances reasoning capabilities by breaking down complex problems into smaller, logical steps.
Key Points:
- Mimics human problem-solving approaches
- Generates intermediate steps leading to final answer
- Significant improvements in multi-step reasoning tasks
- Auto-CoT: Automated version generating multiple reasoning chains
3. Self-Consistency
A decoding strategy generating diverse reasoning chains to select the most consistent final answer.
Key Points:
- Improves accuracy for complex reasoning tasks
- Leverages multiple valid paths to a solution
- Selects most reliable answer by considering various reasoning paths
4. Reflexion and Advanced Search Algorithms
Techniques for continuous learning and efficient decision-making in complex tasks.
Key Points:
- Reflexion: Self-evaluation and adaptation based on previous outputs
- Enables continuous learning and refinement
- LATS: Language Agent Tree Search for optimal solution finding
- Exceptional performance in code generation tasks
5. Parameter Efficient Fine-Tuning (PEFT)
Methods to enhance LLM accessibility and adaptability with minimal computational costs.
Key Points:
- Fine-tunes a smaller number of parameters
- Makes models more efficient and cost-effective
- Achieves high accuracy and adaptability with reduced resources
- Example method: QLoRA
References: arXiv, Open Data Science
