Can LLMs self-design better reasoning workflows?
Self-guided Knowledgeable Network of Thoughts: Amplifying Reasoning with Large Language Models
This paper introduces kNoT (Knowledgeable Network of Thoughts), a new prompting method for LLMs designed to improve multi-step reasoning. It uses the LLM to create its own step-by-step plan (encoded in a structured format called LWT) for solving a task, then executes that plan.
Key points for multi-agent systems: kNoT allows for flexible, network-like execution flows between LLM calls, going beyond simpler chain or tree structures seen in other prompting methods. This enables more complex interaction patterns analogous to communication within a multi-agent system, where each LLM call can be viewed as an agent performing a sub-task. The LWT format also supports accessing individual elements from previous steps' outputs, enabling finer-grained control over information flow between these "agents." This approach aims to reduce the manual prompt engineering often needed for complex tasks and improve performance by breaking them down into smaller, more manageable steps.