Large language models (LLM) such as OpenAI's ChatGPT and GPT-3 offer unique testbeds for exploring the translation challenges of turning literacy into numeracy. Previous publicly-available transformer models from eighteen months prior and 1000 times smaller failed to provide basic arithmetic. The statistical analysis of four complex datasets described here combines arithmetic manipulations that cannot be memorized or encoded by simple rules. The work examines whether next-token prediction succeeds from sentence completion into the realm of actual numerical understanding. For example, the work highlights cases for descriptive statistics on in-memory datasets that the LLM initially loads from memory or generates randomly using python libraries. The resulting exploratory data analysis showcases the model's capabilities to group by or pivot categorical sums, infer feature importance, derive correlations, and predict unseen test cases using linear regression. To extend the model's testable range, the research deletes and appends random rows such that recall alone cannot explain emergent numeracy.

研究表明，大型语言模型在将读写能力转化为计算能力方面存在翻译难题，但通过对所描述的四个复杂数据集的统计分析，结合不能通过简单规则编码或记忆的算术操作，可以推断大型语言模型具备对数据分组/枢轴分类求和、推导相关性、演绎特征权重以及使用线性回归预测海量数据的能力。

由语言模型发展而来的数据科学：从文科到数科