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Approaches to Multi-Step Reasoning in LLMs
Large Language Models can be employed to solve complex reasoning tasks through three distinct methods. The first involves the LLM predicting a conclusion directly, relying on a hidden and uninterpretable internal reasoning mechanism. The second method prompts the LLM to generate a full multi-step reasoning path and the final answer within a single run, as exemplified by Chain-of-Thought. The third approach uses problem decomposition to break the task into sub-problems, which are then addressed in separate LLM interactions or by other specialized systems.
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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
Related
Divide-and-Conquer Paradigm
Example of a Classification Task for LLMs: Identifying AI Risks in a Document
Approaches to Multi-Step Reasoning in LLMs
Two-Step Problem Decomposition
Dynamic Problem Decomposition for Complex Reasoning
Compositionality in NLP
Outlining as a Method of Problem Decomposition for Generative Tasks
General Framework of Problem Decomposition
A team is using a large language model to automate complex tasks. They decide to implement a strategy where a main problem is broken down into a complete, fixed list of sub-problems before the model begins to solve any of them. The model will then solve each sub-problem in sequence. For which of the following tasks is this pre-defined decomposition approach LEAST likely to succeed?
Evaluating a Problem Decomposition Strategy for Multi-Hop QA
Illustrating the Need for Decomposition in Generative Tasks
Complex Reasoning Problems
Multi-hop Question Answering
A development team is building several applications powered by a large language model. Match each application's primary task with the most suitable strategy for breaking down the problem.
Designing a Decomposition-Driven LLM Workflow for a High-Stakes Corporate Task
Debugging a Decomposition-Based LLM Workflow Using Recursive Sub-Problems and Contextual QA Pairs
Evaluating and Redesigning a Decomposition Workflow Under Context and Cost Constraints
Designing a Decomposition-and-QA-Pair Workflow for Contract Review with Recursive Escalation
Stabilizing a Decomposition-Based LLM Workflow for a Regulated Customer-Email Triage System
Designing a Decomposition Workflow for Root-Cause Analysis of a Production Incident
Create a Recursive, Context-Carrying Decomposition Plan for LLM-Assisted KPI Narrative Generation
You are building an internal LLM assistant to answ...
You are designing an internal LLM workflow to answ...
You’re building an internal LLM workflow to answer...
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
Psychological Perspective on Problem Decomposition
Tool Use as Problem Decomposition in LLMs
Learn After
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.