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Improving Problem Decomposition in Least-to-Most Prompting
Beyond applying advanced prompting techniques to existing sub-problems, the least-to-most method can also be improved by developing better methods for the initial problem decomposition and for organizing the subsequent problem-solving path.
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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.
Your team is rolling out an internal LLM assistant...
You’re building an internal LLM workflow to produc...
You’re building an internal LLM assistant to help ...
You’re leading an internal enablement team buildin...
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
Formula for Representing Problem Decomposition
Symbol p0 in Problem Decomposition
A complex reasoning problem is presented to a large language model. The goal is to break the problem down into a series of simpler, sequential sub-problems to guide the model to the correct answer.
Problem: "Alice, Bob, and Carol are a doctor, a lawyer, and an engineer, but not necessarily in that order. The doctor is Carol's sister. Bob is not the lawyer. Who is the engineer?"
Which of the following sequences of sub-problems represents the most effective decomposition and problem-solving path for the model to follow?
Critiquing a Problem-Solving Path
Analyzing a Flawed Problem Decomposition Strategy
To solve a complex reasoning problem, it is often broken down into a series of simpler sub-problems that are solved sequentially. For the main problem below, arrange the provided sub-problems into the most logical and effective sequence for a model to follow.
Main Problem: A company has two data centers, A and B. Data center A has 500 servers, and each server consumes 400 watts of power. Data center B has 300 servers, and each server consumes 600 watts. If the cost of electricity is $0.12 per kilowatt-hour (kWh), what is the total daily electricity cost for both data centers combined?