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📓 Number Cookbook: Number Understanding of Language Models and How to Improve It
This research paper examines the numerical understanding and processing abilities (NUPA) of large language models (LLMs). The authors create a benchmark to test LLMs on four numerical representations (integers, floating-point numbers, fractions, and scientific notation) across 17 tasks grouped into four ability categories. They find that, despite strong problem-solving capabilities, LLMs struggle with basic numerical operations. The paper evaluates methods to enhance NUPA during pretraining and finetuning, such as specialized tokenizers, positional encodings, and data formats, and notes the limitations of chain-of-thought techniques for numerical tasks. The authors call for further research to improve LLMs' fundamental numerical capabilities.
📎 Link to paper
This research paper examines the numerical understanding and processing abilities (NUPA) of large language models (LLMs). The authors create a benchmark to test LLMs on four numerical representations (integers, floating-point numbers, fractions, and scientific notation) across 17 tasks grouped into four ability categories. They find that, despite strong problem-solving capabilities, LLMs struggle with basic numerical operations. The paper evaluates methods to enhance NUPA during pretraining and finetuning, such as specialized tokenizers, positional encodings, and data formats, and notes the limitations of chain-of-thought techniques for numerical tasks. The authors call for further research to improve LLMs' fundamental numerical capabilities.
📎 Link to paper
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