Why LLMs Struggle with Numbers: A Practical Approach with LightGBM

research #llm 📝 Blog|Analyzed: Jan 12, 2026 09:00•

Published: Jan 12, 2026 08:58

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1 min read

Analysis

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.

Key Takeaways

•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.

Reference / Citation

View Original

"The article begins by stating the common misconception that LLMs like ChatGPT and Claude can perform highly accurate predictions using Excel files, before noting the fundamental limits of the model."

Qiita AIJan 12, 2026 08:58

* Cited for critical analysis under Article 32.

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