Exploring Internal Numeracy in Language Models: A Case Study on ALBERT

arXiv:2404.16574v1 Announce Type: new
Abstract: It has been found that Transformer-based language models have the ability to perform basic quantitative reasoning. In this paper, we propose a method for studying how these models internally represent numerical data, and use our proposal to analyze the ALBERT family of language models. Specifically, we extract the learned embeddings these models use to represent tokens that correspond to numbers and ordinals, and subject these embeddings to Principal Component Analysis (PCA). PCA results reveal that …

abstract albert analyze arxiv basic case case study cs.cl data family found language language models numerical paper quantitative quantitative reasoning reasoning study studying transformer type