What is it about?
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.
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Why is it important?
Since 2020, an increasing number of AI systems have been introduced that are able to successfully complete complex text generation tasks in natural language processing (NLP), such as text summarisation, translation, and question answering. More recently, the concept of generation has even been extended to multi-modal approaches involving, for example, text input and image output. Many of these systems have demonstrated NLP capabilities at a level very close-to-human capabilities. We propose to use and extend Boxology to gain insight into a variety of LLMs, specifically on LLMs used in a neuro-symbolic approach.
Perspectives
This paper provides two contributions: Firstly, we propose novel design patterns as an extension of the current Boxology to promote transparency and trustworthiness in system design, by providing interpretable, high-level component descriptions of LLM-based neuro-symbolic systems. Our modular approach supports new architectures and engineering approaches to LLM-based systems. Secondly, we test validity and usefulness of the Boxology and our extensions in this field on example architectures and applications, such as ChatGPT, KnowGL, GENOME and Logic-LM.
André Meyer-Vitali
Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI)
Read the Original
This page is a summary of: Design Patterns for Large Language Model Based Neuro-Symbolic Systems, Neurosymbolic Artificial Intelligence, September 2025, SAGE Publications,
DOI: 10.1177/29498732251377499.
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