Summary of Enhancing Llm Problem Solving with Reap: Reflection, Explicit Problem Deconstruction, and Advanced Prompting, by Ryan Lingo et al.
Enhancing LLM Problem Solving with REAP: Reflection, Explicit Problem Deconstruction, and Advanced Prompting
by Ryan Lingo, Martin Arroyo, Rajeev Chhajer
First submitted to arxiv on: 14 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have revolutionized natural language processing, but improving their problem-solving capabilities for complex tasks remains a challenge. The REAP method is introduced as an innovative approach within the dynamic context generation framework. It guides LLMs through reflection on the query, deconstruction of manageable components, and generation of relevant context to enhance the solution process. This paper compares 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 show notable performance gains for most models, with improvements ranging from 40.97% to 112.93%. REAP also offers a cost-effective solution, improving model outputs’ clarity and making it easier for humans to understand the reasoning behind the results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can help us with many tasks, like understanding language. But they sometimes struggle with really tricky problems. The REAP method is a new way to make LLMs better at solving these complex problems. It helps them think about what the problem is asking and then gives them useful information to make a good answer. Scientists tested this new approach using six different models, like OpenAI’s o1-preview and Google’s Gemini 1.5 Pro. They found that REAP helped most of the models do better, with some even getting much better! This means we might be able to use LLMs for more things, like helping us understand tricky ideas or making decisions. |
Keywords
» Artificial intelligence » Claude » Gemini » Gpt » Natural language processing » Prompting » Zero shot
