This research paper delves into the shortcomings of current Large Language Models (LLMs) when tackling complex reasoning tasks, such as understanding errors and calculation process errors. The authors propose the Deeply Understanding the Problems (DUP) method to significantly enhance LLMs’ reasoning capabilities. Key insights include:

• Improved performances across 10 diverse reasoning benchmarks.
• Achieving a new State-of-the-Art (SOTA) accuracy score of 97.1% on the GSM8K benchmark in a zero-shot setting.

Why This Research Matters:

The DUP method offers a groundbreaking approach to understanding and solving complex problems by allowing LLMs to attain a deeper comprehension than before. This method has the potential to significantly improve how LLMs are trained for real-world applications involving complex problem-solving scenarios, particularly in fields such as healthcare and legal advisories.