MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning

Abstract: Huge LMs have ushered in a new era for AI, serving as a gateway to natural-language-based knowledge tasks. Although an essential element of modern AI, LMs are also inherently limited in a number of ways. We discuss these limitations and how they can be avoided by adopting a systems approach. Conceptualizing the challenge as one that involves knowledge and reasoning in addition to linguistic processing, we define a flexible architecture with multiple neural models, complemented by discrete knowledge and reasoning modules. We describe this neuro-symbolic architecture, dubbed the Modular Reasoning, Knowledge and Language (MRKL, pronounced "miracle") system, some of the technical challenges in implementing it, and Jurassic-X, AI21 Labs' MRKL system implementation.

• The paper presents MRKL as a modular neuro-symbolic system that overcomes LLM limitations by routing queries to specialized expert modules.
• It details the integration of neural models with symbolic reasoning, demonstrated by Jurassic-X's reliable performance in complex arithmetic operations.
• The research implies that MRKL systems can boost AI accuracy and flexibility by combining dynamic knowledge access with robust discrete reasoning.

Modular Reasoning, Knowledge, and Language Systems: Addressing Limitations of LLMs

The paper presents a thoughtful exploration of the limitations inherent in LLMs and proposes an architecture called Modular Reasoning, Knowledge, and Language (MRKL) systems to address these issues. This neuro-symbolic approach integrates multiple neural models with discrete knowledge and reasoning modules, creating a system capable of overcoming the drawbacks of standard LLMs that are increasingly recognized in advanced AI research.

The MRKL architecture is noteworthy for its modularity, offering a flexible design that allows for the inclusion of specialized modules termed "experts". These can be either neural or symbolic, facilitating a hybrid system that merges the strengths of both approaches. The paper identifies critical limitations in LLMs, including their difficulty in maintaining up-to-date information, lack of access to proprietary data, and inability to perform certain reasoning tasks efficiently. MRKL systems offer an elegant solution by using a router module to direct queries to the appropriate expert, thereby handling varied tasks effectively.

The implementation of Jurassic-X by AI21 Labs serves as a practical demonstration of the MRKL architecture. Jurassic-X combines LLMs with external APIs and symbolic computation tools to enhance its functionality. The real-world applicability and potential of MRKL systems are further evidenced through its ability to integrate dynamic knowledge, access proprietary databases, and perform symbolic reasoning. This implementation exemplifies the robustness, interpretability, and extensibility that MRKL systems promise.

One significant empirical focus of the paper is the integration of a symbolic calculator within Jurassic-X, showcasing the system's ability to bridge the neuro-symbolic divide. The researchers systematically trained the model to extract arguments for basic arithmetic operations, achieving high reliability. Several experiments explored the generalization capabilities across different arithmetic problem formats, operations, and problem length variables. MRKL systems demonstrated potential in this domain, outperforming traditional LLMs.

The implications of this research are manifold. Practically, MRKL systems could enhance the effectiveness and accuracy of AI applications across various industries by leveraging their flexible and modular nature. Theoretically, this approach presents an innovative pathway for advancing neuro-symbolic AI methodologies, suggesting future explorations into further integrating and balancing neural and symbolic systems.

Future developments are likely to further establish MRKL systems within the pantheon of AI architectures, contributing to more intelligent systems that combine the abstract power of neural networks with the concrete stability of symbolic reasoning. This research invites further inquiry into expanding the range of expert modules and investigating new applications for MRKL structures, potentially shaping the future of AI development. The insights presented in this paper pave the way for ongoing improvements in AI systems, addressing limitations and inviting advancements that are both promising and necessary.