Arbitrary-Length Generalization for Addition in a Tiny Transformer

• URL: http://arxiv.org/abs/2406.00075v2
• Date: Wed, 12 Jun 2024 03:40:35 GMT
• Title: Arbitrary-Length Generalization for Addition in a Tiny Transformer
• Authors: Alexandre Galvao Patriota,
• Abstract summary: This paper introduces a novel training methodology that enables a Transformer model to generalize the addition of two-digit numbers to numbers with unseen lengths of digits. The proposed approach employs an autoregressive generation technique, processing from right to left, which mimics a common manual method for adding large numbers.
• Score: 55.2480439325792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces a novel training methodology that enables a Transformer model to generalize the addition of two-digit numbers to numbers with unseen lengths of digits. The proposed approach employs an autoregressive generation technique, processing from right to left, which mimics a common manual method for adding large numbers. To the best of my knowledge, this methodology has not been previously explored in the literature. All results are reproducible, and the corresponding R code is available at github.com/AGPatriota/ALGA-R/.

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