Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Enhancing LLM Problem Solving with REAP: Reflection, Explicit Problem Deconstruction, and Advanced Prompting

Ryan Lingo, Martin Arroyo, Rajeev Chhajer

TL;DR

The REAP (Reflection, Explicit Problem Deconstruction, and Advanced Prompting) method, an innovative approach within the dynamic context generation framework, is introduced, providing both better performance and increased cost-efficiency across a wide range of applications.

Abstract

Large Language Models (LLMs) have transformed natural language processing, yet improving their problem-solving capabilities, particularly for complex, reasoning-intensive tasks, remains a persistent challenge. This paper introduces the REAP (Reflection, Explicit Problem Deconstruction, and Advanced Prompting) method, an innovative approach within the dynamic context generation framework. REAP guides LLMs through reflection on the query, deconstructing it into manageable components, and generating relevant context to enhance the solution process. We evaluated REAP using a dataset designed to expose LLM limitations, comparing zero-shot prompting with REAP-enhanced prompts across six state-of-the-art models: OpenAI's o1-preview, o1-mini, GPT-4o, GPT-4o-mini, Google's Gemini 1.5 Pro, and Claude 3.5 Sonnet. The results demonstrate notable performance gains, with o1-mini improving by 40.97%, GPT-4o by 66.26%, and GPT-4o-mini by 112.93%. Despite the already strong baseline performance of OpenAI's o1-preview, modest gains were observed. Beyond performance improvements, REAP offers a cost-effective solution; for example, GPT-4o-mini, which is approximately 100 times cheaper than o1-preview, delivered competitive results. REAP also improves the clarity of model outputs, making it easier for humans to understand the reasoning behind the results and simplifying the process of identifying and addressing any issues. These findings demonstrate REAP's potential to greatly improve the capabilities of LLMs, providing both better performance and increased cost-efficiency across a wide range of applications.

Enhancing LLM Problem Solving with REAP: Reflection, Explicit Problem Deconstruction, and Advanced Prompting

TL;DR

The REAP (Reflection, Explicit Problem Deconstruction, and Advanced Prompting) method, an innovative approach within the dynamic context generation framework, is introduced, providing both better performance and increased cost-efficiency across a wide range of applications.

Abstract

Large Language Models (LLMs) have transformed natural language processing, yet improving their problem-solving capabilities, particularly for complex, reasoning-intensive tasks, remains a persistent challenge. This paper introduces the REAP (Reflection, Explicit Problem Deconstruction, and Advanced Prompting) method, an innovative approach within the dynamic context generation framework. REAP guides LLMs through reflection on the query, deconstructing it into manageable components, and generating relevant context to enhance the solution process. We evaluated REAP using a dataset designed to expose LLM limitations, comparing zero-shot prompting with REAP-enhanced prompts across six state-of-the-art models: OpenAI's o1-preview, o1-mini, GPT-4o, GPT-4o-mini, Google's Gemini 1.5 Pro, and Claude 3.5 Sonnet. The results demonstrate notable performance gains, with o1-mini improving by 40.97%, GPT-4o by 66.26%, and GPT-4o-mini by 112.93%. Despite the already strong baseline performance of OpenAI's o1-preview, modest gains were observed. Beyond performance improvements, REAP offers a cost-effective solution; for example, GPT-4o-mini, which is approximately 100 times cheaper than o1-preview, delivered competitive results. REAP also improves the clarity of model outputs, making it easier for humans to understand the reasoning behind the results and simplifying the process of identifying and addressing any issues. These findings demonstrate REAP's potential to greatly improve the capabilities of LLMs, providing both better performance and increased cost-efficiency across a wide range of applications.
Paper Structure (80 sections, 3 figures, 7 tables)

This paper contains 80 sections, 3 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Context and Importance
  3. REAP: A Structured Methodology for Enhancing LLM Problem-Solving
  4. Research Focus and Objectives
  5. Contributions
  6. Literature Review: Evolution of Problem-Solving Techniques in Large Language Models
  7. Problem Statement
  8. Current Limitations in Prompting Strategies
  9. The Need for Integration: The REAP Approach
  10. Research Hypothesis
  11. Expected Outcomes
  12. Proposed Approach: REAP
  13. Overview of the REAP Method
  14. Novel Contributions
  15. Components of the REAP Method
  16. ...and 65 more sections

Figures (3)

  • Figure 1: Comparison between Zero-Shot and Dynamic Context Generation (DCG) methodologies in LLMs. The DCG approach involves generating and applying context to enhance the final output, while Zero-Shot directly produces output from the input.
  • Figure 2: Overview of the REAP Method: Reflection, Explicit Problem Deconstruction, and Advanced Prompting, with their respective components.
  • Figure 3: Illustration of the evolutionary path of problem-solving techniques in LLMs, culminating in the development of the REAP methodology.