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

Numeracy from Literacy: Data Science as an Emergent Skill from Large Language Models