xVal: A Continuous Numerical Tokenization for Scientific Language Models

• URL: http://arxiv.org/abs/2310.02989v2
• Date: Sun, 15 Dec 2024 07:07:28 GMT
• Title: xVal: A Continuous Numerical Tokenization for Scientific Language Models
• Authors: Siavash Golkar, Mariel Pettee, Michael Eickenberg, Alberto Bietti, Miles Cranmer, Geraud Krawezik, Francois Lanusse, Michael McCabe, Ruben Ohana, Liam Parker, Bruno Régaldo-Saint Blancard, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho,
• Abstract summary: We introduce xVal, a strategy for continuously tokenizing numbers within language models. We train specially-modified language models from scratch on a variety of scientific datasets formatted as text.
• Score: 41.26924657687872
• License:
• Abstract: Due in part to their discontinuous and discrete default encodings for numbers, Large Language Models (LLMs) have not yet been commonly used to process numerically-dense scientific datasets. Rendering datasets as text, however, could help aggregate diverse and multi-modal scientific data into a single training corpus, thereby potentially facilitating the development of foundation models for science. In this work, we introduce xVal, a strategy for continuously tokenizing numbers within language models that results in a more appropriate inductive bias for scientific applications. By training specially-modified language models from scratch on a variety of scientific datasets formatted as text, we find that xVal generally outperforms other common numerical tokenization strategies on metrics including out-of-distribution generalization and computational efficiency.

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