Why LLMs Struggle with Numbers: A Practical Approach with LightGBM

This article highlights a crucial limitation of large language models (LLMs) - their difficulty with numerical tasks. It correctly points out the underlying issue of tokenization and suggests leveraging specialized models like LightGBM for superior numerical prediction accuracy. This approach underlines the importance of choosing the right tool for the job within the evolving AI landscape.

• •LLMs often struggle with numerical data due to their tokenization process.
• •The article advocates for using specialized models like LightGBM for numerical predictions.
• •This approach suggests a hybrid strategy of LLMs for text and other models for specific tasks.