reap

Background:
The REAP method (Reflection, Explicit Problem Deconstruction, and Advanced Prompting) enhances Large Language Models (LLMs) for complex, multi-step tasks. It integrates reflection, systematic task breakdown, and advanced prompting to improve coherence, clarity, and relevance—while maintaining cost-efficiency and scalability.
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Methods:
REAP promotes iterative refinement (reflection), clear decomposition of tasks into manageable parts, and advanced prompting to generate dynamic contexts and explore solution paths. This approach was tested on multiple leading LLMs (GPT-4 variants, Anthropic’s Claude, Google’s Gemini) with a carefully designed dataset to expose reasoning gaps.
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Findings:
By leveraging REAP, we observed performance gains of up to 112% in some models, along with more coherent and contextually relevant outputs. Additionally, REAP offers cost-efficiency by enabling lower-performing models to reach competitive levels, and fosters improved explainability by providing clearer outputs that simplify both trust and error identification.
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Learn more at: arxiv.org/abs/2409.09415