"Self-guided Knowledgeable Network of Thoughts: Amplifying Reasoning with Large Language Models"

LLMs become their own task planners with kNoT's (Knowledgeable Network of Thoughts) flexible network of simple operations.

kNoT introduces a self-guided prompting system where LLMs create their own solution plans and execute them through a flexible network of elementary operations.

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https://arxiv.org/abs/2412.16533

🤔 Original Problem:

Existing prompt engineering methods like Chain-of-Thought and Tree of Thoughts require extensive manual configuration and struggle with complex reasoning tasks. They also lack flexibility in reasoning structures and precision in handling larger inputs.

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🛠️ Solution in this Paper:

→ kNoT uses a novel LLM Workflow Template (LWT) that enables LLMs to create executable plans for themselves.

→ The system first extracts knowledge to generate a solution plan, then translates it into LWT format.

→ LWT allows message passing between different reasoning steps through input fields and indexing.

→ Each operation is broken down into elementary steps for precise control and better accuracy.

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💡 Key Insights:

→ LLMs can effectively plan and execute their own reasoning strategies when given proper structure

→ Breaking down complex tasks into elementary operations improves accuracy

→ Flexible network structures outperform rigid reasoning frameworks

→ Automated task planning reduces human engineering effort

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📊 Results:

→ Achieved 92% accuracy in sorting 32 numbers vs 12% (ToT) and 31% (GoT)

→ Reduced task-specific prompts by up to 84.4% vs ToT and 87.3% vs GoT

→ Maintained strong performance on larger inputs where other methods fail completely