Implementing Derivations of Definite Logic Programs with Self-Attention Networks

Phan Thi Thanh Thuy; Akihiro Yamamoto

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arXiv · 2410.11396

Implementing Derivations of Definite Logic Programs with Self-Attention Networks

Phan Thi Thanh Thuy·Akihiro Yamamoto

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Abstract

In this paper we propose that a restricted version of logical inference can be implemented with self-attention networks. We are aiming at showing that LLMs (Large Language Models) constructed with transformer networks can make logical inferences. We would reveal the potential of LLMs by analyzing self-attention networks, which are main components of transformer networks. Our approach is not based on semantics of natural languages but operations of logical inference. %point of view. We show that hierarchical constructions of self-attention networks with feed forward networks (FFNs) can implement top-down derivations for a class of logical formulae. We also show bottom-up derivations are also implemented for the same class. We believe that our results show that LLMs implicitly have the power of logical inference.

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BibTeX RIS

Phan Thi Thanh Thuy, Akihiro Yamamoto. 2024-10-15. Implementing Derivations of Definite Logic Programs with Self-Attention Networks. https://arxiv.org/abs/2410.11396

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