This research paper investigates the numerical understanding and processing abilities (NUPA) of large language models (LLMs). The authors introduce a benchmark, covering various numerical representations and tasks, to systematically evaluate LLMs' capabilities in handling numbers. The paper finds that while LLMs perform well on simpler tasks, their performance deteriorates significantly as task complexity and input length increase. The authors also explore various techniques to improve NUPA, including specialized tokenizers, positional encodings, and data formats. Despite some successes in improving NUPA during pre-training, these techniques are found to be ineffective when applied to already trained models. The paper concludes that further research is necessary to address the challenges of NUPA in LLMs and enable them to confidently handle numerical tasks in real-world applications.

Ep46. Number Cookbook: Number Understanding of Language Models and How to Improve It

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Ep46. Number Cookbook: Number Understanding of Language Models and How to Improve It

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