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arXiv · 2407.17771

Banyan: Improved Representation Learning with Explicit Structure

Mattia Opper·N. Siddharth
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Abstract

We present Banyan, a model that efficiently learns semantic representations by leveraging explicit hierarchical structure. While transformers excel at scale, they struggle in low-resource settings. Conversely recent structured models have shown promise as efficient learners, but lack performance. Banyan bridges this gap with two key innovations: an entangled hierarchical tree structure and diagonalized message passing, enabling it to outperform larger transformer models with just 14 non-embedding parameters. It excels in low-resource settings, offering a viable alternative for under-represented languages and highlighting its potential for efficient, interpretable NLP in resource-constrained environments.

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Mattia Opper, N. Siddharth. 2024-07-25. Banyan: Improved Representation Learning with Explicit Structure. https://arxiv.org/abs/2407.17771

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