Mastering Multi-Hop Reasoning

Take your prompt engineering skills to the next level by learning how to craft prompts that enable AI models to solve complex, multi-step problems. This article dives deep into multi-hop reasoning, providing practical examples and code snippets to help you master this powerful technique.

Imagine asking a large language model (LLM) to solve a riddle like this:

“John is taller than Mary. Mary is taller than Peter. Who is the shortest?”

Humans can easily deduce that Peter is the shortest. We intuitively understand the relationships between height and apply them across multiple steps. But how do we teach LLMs to perform such “multi-hop reasoning”?

This is where multi-hop reasoning prompts come into play. They are a powerful technique that allows us to break down complex problems into smaller, interconnected steps, guiding the LLM through a logical chain of thought.

Why Multi-Hop Reasoning Matters:

Traditional prompt engineering often relies on single-step instructions. However, many real-world tasks require multi-step reasoning, such as:

• Solving logic puzzles and riddles: As illustrated above, LLMs need to understand relationships and apply them sequentially.
• Summarizing complex texts: Accurately summarizing a lengthy article might involve identifying key themes, understanding arguments, and connecting different ideas across paragraphs.
• Generating creative content with plot twists: Crafting a compelling story often involves building suspense by introducing unexpected events and consequences.

Crafting Effective Multi-Hop Reasoning Prompts:

Here’s a breakdown of how to create multi-hop reasoning prompts:

1. Identify the Key Steps: Analyze the problem and break it down into distinct logical steps. For the height riddle, the steps are:

   • “John is taller than Mary.”
   • “Mary is taller than Peter.”
   • Deduce who is shortest.

2. Structure the Prompt: Guide the LLM through each step with clear instructions. Use phrasing that emphasizes relationships and comparisons:

   prompt = """
   Here's a riddle:
   
   John is taller than Mary. 
   Mary is taller than Peter. 
   
   Who is the shortest person? Explain your reasoning.
   """

3. Encourage Explanation: Prompt the LLM to explicitly state its reasoning at each step. This helps identify potential flaws in logic and ensures transparency:

   prompt = """
   Here's a riddle:
   
   John is taller than Mary. 
   Mary is taller than Peter. 
   
   Who is the shortest person? Explain your reasoning step-by-step. 
   """

Example Output:

A well-crafted multi-hop reasoning prompt might elicit the following response from an LLM:

Peter is the shortest person. Here's how I figured that out:

1. John is taller than Mary. This means Mary is shorter than John.

2. Mary is taller than Peter. This means Peter is shorter than Mary.

3. Since Peter is shorter than Mary, and Mary is shorter than John, Peter must be the shortest of the three. 

Beyond Simple Examples:

Multi-hop reasoning can be applied to far more complex scenarios. For example, you could use it to:

• Generate code: Break down a programming task into smaller functions and guide the LLM to write each part sequentially.
• Analyze scientific data: Prompt the LLM to identify trends, draw conclusions, and propose further research questions based on experimental results.

Mastering multi-hop reasoning is essential for unlocking the full potential of LLMs. By breaking down complex problems into manageable steps and encouraging explicit reasoning, you can empower AI models to solve tasks that were previously beyond their reach.