Prompting Strategies: Structured Reasoning

Let’s explore some of the structured reasoning techniques:

CoT prompts the model to articulate its reasoning process step-by-step, imitating human problem-solving patterns. The distinctive qualities of CoT prompting highlight its ability to steer LLMs toward a logical reasoning chain. This not only clarifies how the model reaches conclusions but also tends to improve the overall quality of the responses.

Example

User prompt:
How can I calculate the area of a circle with a radius of 5 units?

Prompt template:
To solve the query: '{user_input}', explain your thought process step by step.

• Start by identifying the formula needed for the calculation, then substitute the known values, and finally compute the result.

• For instance, if asked to calculate the volume of a sphere, you would start by stating the formula (Volume = 4/3 * pi * radius^3), substituting the given radius, and performing the arithmetic to find the volume.

An extension of CoT, ToT structures the reasoning process into a tree of intermediate steps, enhancing the model’s ability to explore different reasoning pathways and arrive at a well-rounded solution. ToT manages a tree structure of intermediate reasoning steps known as thoughts. Each thought represents a coherent language sequence moving toward the final solution. This structure allows language models to reason by assessing the progress generated by thoughts in solving the problem.

Example

User prompt:
How should I distribute my investment of $10,000 among stocks, bonds, and savings to maximize my returns next year, considering varying risk factors?

Prompt template:
To address the query: '{user_input}', build a reasoning tree.

• Start with the initial decision points: assessing risk levels of stocks, bonds, and savings.
  Then, branch out into potential returns based on historical data and expert predictions for each category.

• For each branch, consider factors like market volatility and economic forecasts to refine your advice.

• Finally, converge these thoughts to propose an optimal distribution strategy that balances potential returns with risk tolerance.

• Example steps might include calculating expected returns for conservative estimates, comparing these against aggressive strategies, and recommending a balanced approach based on the user’s risk profile.

Advancing beyond linear and tree-based reasoning, GoT employs a graph-based approach to better represent the non-linear nature of human thought processes, allowing for more complex interconnections between different reasoning elements. This approach allows dynamic interplay, backtracking, and evaluation of ideas, facilitating the aggregation and combination of thoughts from different branches. The main contributions include modeling the reasoning process as a directed graph ...