Design Patterns for Large Language Model Based Neuro-Symbolic Systems

de Boer, Maaike and Smit, Quirine and van Bekkum, Michael and Meyer-Vitali, André and Schmid, Thomas (2025) Design Patterns for Large Language Model Based Neuro-Symbolic Systems. Neurosymbolic Artificial Intelligence, 1. ISSN 2949-8732

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Abstract

Large language models (LLMs) have been a dominating trend in artificial intelligence (AI) in the past years. At the same time, neuro-symbolic systems employing LLMs have also received increasing interest due to their advantages over purely statistical generative models: They can make explicit use of expert knowledge and can be understood and inspected by humans thus providing explainability. However, with an increasing variety of approaches, it is currently difficult to compare the different ways in which designing, training, fine-tuning, and applying such approaches take place. In this work, we use and extend the modular design patterns for hybrid learning and reasoning systems and the Boxology language of van Bekkum et al. for this purpose. These patterns provide a general language to describe, compare, and understand the different architectures and methods used for LLM-based neuro-symbolic systems. The primary goal of this work is to support a better understanding of specific classes of such systems, namely LLM-based models that are used in conjunction with knowledge-based (symbolic) systems. In order to demonstrate the usefulness of this approach, we explore existing LLM-based neuro-symbolic architectures and approaches, as well as use cases for these design patterns.