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Sub-problem Generation in Least-to-Most Prompting
Within the least-to-most prompting framework, the generation of sub-problems is accomplished by prompting a Large Language Model. This process can be guided by providing the LLM with specific instructions and/or demonstrations, such as few-shot examples, to effectively break down the larger problem.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Sub-problem Generation in Least-to-Most Prompting
Improving Least-to-Most Prompting with Advanced Techniques
Improving Problem Decomposition in Least-to-Most Prompting
An AI developer needs a large language model to solve a complex, multi-step logic puzzle that requires deducing a final answer from a series of interdependent clues. Initial attempts to solve the puzzle by providing the full puzzle and a few examples of other solved puzzles have consistently failed. Which of the following prompting strategies is the most effective next step, and why?
Analyzing a Problem-Solving Approach
A language model is tasked with solving the following logic puzzle: 'Sarah, David, and Emily are a doctor, a lawyer, and an engineer. The doctor is Emily's sister. David is not the lawyer.' To solve this complex problem, it is broken down into a series of simpler, sequential sub-problems. Arrange the following sub-problems in the correct logical order that builds towards the final solution.
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Choosing and Justifying a Prompting Strategy Under Context and Quality Constraints
Designing a Prompting Workflow for a High-Stakes, Multi-Step Task
Diagnosing and Redesigning a Prompting Approach for a Decomposed Workflow
Stabilizing an LLM Workflow for Multi-Step Policy Compliance Decisions
Debugging a Multi-Step LLM Workflow for Contract Clause Risk Triage
Designing a Robust Prompting Workflow for Multi-Step Root-Cause Analysis with Limited Examples
Example of Final Problem Solving in Least-to-Most Prompting
Learn After
Example of an Instructional Prompt in a Few-Shot Setting for Sub-Problem Decomposition
Example of LLM Generating Sub-Problems for a Duration Question
A developer is using a large language model to solve a complex, multi-step reasoning problem. The goal is for the model to first break the problem down into a sequence of simpler sub-problems and then solve them in order. The developer provides the model with the complex problem and the simple instruction: 'Here is a problem. Solve it.' The model attempts to answer directly but fails. Which of the following best explains why the model failed to break the problem down as intended?
Sequential Sub-Problem Solving with Contextual QA Pairs
A developer wants to guide a Large Language Model to break down a complex problem into simpler sub-problems. Arrange the following components into the most effective and logical sequence for a one-shot prompt to accomplish this task.
Guiding an LLM for Problem Decomposition
Formula for Least-to-Most Sub-Problem Generation