I’m currently the Chief Evangelist @ HumanFirst. I explore & write about all things at the intersection of AI & language; ranging from LLMs, Chatbots, Voicebots, Development Frameworks, Data-Centric latent spaces & more.
Least-To-Most Prompting
The process of inference is reaching a conclusion based on evidence and reasoning. And in turn reasoning can be engendered with LLMs by providing the LLM with a few examples on how to reason and use evidence.
Hence a novel prompting strategy was developed, named least-to-most prompting. This method is underpinned by the following strategy:
- Decompose a complex problem into a series of simpler sub-problems.
- And subsequently solving for each of these sub-questions.
Solving each subproblem is facilitated by the answers to previously solved subproblems.
Hence least to most prompting is a technique of using a progressive sequence of prompts to reach a final conclusion.