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Example of Final Problem Solving in Least-to-Most Prompting
In least-to-most prompting, after all intermediate sub-problems have been sequentially answered, the final solution is derived by providing the language model with the original problem alongside the full context of all previously generated sub-problems and their corresponding answers. For example, to answer the original question "What was the duration of the environmental study?", the model receives the context that the study started in 2015 and ended in 2020. Using this comprehensive context, the model correctly synthesizes the final answer: the duration was 5 years.
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Foundations of Large Language Models
Ch.3 Prompting - 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
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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