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Due in part to their discontinuous and discrete default encodings fornumbers, Large Language Models (LLMs) have not yet been commonly used toprocess numerically-dense scientific datasets. Rendering datasets as text,however, could help aggregate diverse and multi-modal scientific data into asingle training corpus, thereby potentially facilitating the development offoundation models for science. In this work, we introduce xVal, a strategy forcontinuously tokenizing numbers within language models that results in a moreappropriate inductive bias for scientific applications. By trainingspecially-modified language models from scratch on a variety of scientificdatasets formatted as text, we find that xVal generally outperforms othercommon numerical tokenization strategies on metrics includingout-of-distribution generalization and computational efficiency.

xVal: A Continuous Numerical Tokenization for Scientific Language Models