A Novel Spinor-Based Embedding Model for Transformers

TL;DR

This paper introduces a new word embedding method for Transformers using spinors from geometric algebra, aiming to improve the expressiveness and robustness of language models by capturing complex relationships.

Contribution

It presents the theoretical foundation and integration of spinor-based embeddings into Transformer models, a novel approach in language representation.

Findings

Enhanced expressiveness of language representations

Potential robustness improvements in Transformer models

Theoretical framework for spinor integration

Abstract

This paper proposes a novel approach to word embeddings in Transformer models by utilizing spinors from geometric algebra. Spinors offer a rich mathematical framework capable of capturing complex relationships and transformations in high-dimensional spaces. By encoding words as spinors, we aim to enhance the expressiveness and robustness of language representations. We present the theoretical foundations of spinors, detail their integration into Transformer architectures, and discuss potential advantages and challenges.