Direct Conclusion Generation with Hidden Reasoning

Single-Run Multi-Step Reasoning

Multi-Run Problem Decomposition for Complex Reasoning

Self-Refinement in LLMs

Predict-then-Verify Approaches in LLM Reasoning

Principle of Generating Longer Reasoning Paths

Modifying Decoding for Longer Reasoning Paths

Multi-Stage Generation for Incremental Reasoning

An engineer is building a system to solve complex logic puzzles. When a puzzle is submitted, the system sends a single, carefully crafted prompt to a large language model. The model's output is a complete, step-by-step explanation of how it solved the puzzle, followed by the final answer, all generated in one response. Which approach to multi-step reasoning does this system exemplify?

Prompting for a Reasoning Process to Mitigate Errors in Complex Tasks

Compositional Generalization in LLMs

Choosing a Reasoning Strategy for a Financial AI

You are designing systems that use a large language model to solve complex problems. Match each system description with the reasoning approach it employs.

Evaluating a Novel Prompting Strategy

A researcher is trying to get a language model to solve a multi-step logic puzzle. They test two different prompts:

Prompt A: 'What is the solution to the following logic puzzle? [Puzzle text]'

Prompt B: 'Solve the following logic puzzle. First, break down the puzzle into individual facts and constraints. Next, reason through the implications of each fact step-by-step. Finally, state your conclusion and explain how you arrived at it. [Puzzle text]'

Which statement best analyzes why Prompt B is likely to yield a more accurate solution for this type of task?

Evaluating LLM Reasoning Outputs

Explicit Prompting for Extended Deliberation

Modifying Decoding for Longer Reasoning Paths

Multi-Stage Generation for Incremental Reasoning