Few-shot prompts
Allows us to provide exemplars in prompts to steer the model towards better performance.
Chain-of-Thought (CoT) Prompting
- Instructing the model to reason about the task when responding
- Can be combined with few-shot prompting to get better results
- Useful for tasks that requires reasoning
1 | The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1 |
Zero-shot CoT
Involves adding “Let’s think step by step” to the original prompt
Generate Knowledge Prompting
- Using additional knowledge provided as part of the context to improve results on complex tasks such as commonsense reasoning
- The knowledge used in the context is generated by a model and used in the prompt to make a prediction. The knowledge samples are then used to generate knowledge augmented questions to get answer proposals
Program-aided Language Model (PAL)
Program-aided language models (PAL) uses an LLM to read problems and generate programs as the intermediate reasoning steps.
It offloads the solution step to a runtime such as Python interpreter
ReAct
- ReAct is a framework where LLMs are used to generate both reasoning traces and task-specific actions in an interleaved manner
- Generating reasoning traces allow the model to induce, track, and update actions plans and handle exceptions
- The action step allows to interface with and gather information from external sources such as knowlege bases or environments
- ReAct allows LLMs to interact with external tools to retrieve additional information that leads to more reliable and factual responses