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

Ehud Karpas; Omri Abend; Yonatan Belinkov; Barak Lenz; Opher Lieber; Nir Ratner; Yoav Shoham; Hofit Bata; Yoav Levine; Kevin Leyton-Brown; Dor Muhlgay; Noam Rozen; Erez Schwartz; Gal Shachaf; Shai Shalev-Shwartz; Amnon Shashua; Moshe Tenenholtz

arXiv · 2205.00445

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

Ehud Karpas·Omri Abend·Yonatan Belinkov·Barak Lenz·Opher Lieber·Nir Ratner·Yoav Shoham·Hofit Bata·Yoav Levine·Kevin Leyton-Brown·Dor Muhlgay·Noam Rozen·Erez Schwartz·Gal Shachaf·Shai Shalev-Shwartz·Amnon Shashua·Moshe Tenenholtz

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Abstract

Huge language models (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.

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Ehud Karpas, Omri Abend, Yonatan Belinkov, Barak Lenz, Opher Lieber, Nir Ratner, Yoav Shoham, Hofit Bata, Yoav Levine, Kevin Leyton-Brown, Dor Muhlgay, Noam Rozen, Erez Schwartz, Gal Shachaf, Shai Shalev-Shwartz, Amnon Shashua, Moshe Tenenholtz. 2022-05-01. MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning. https://arxiv.org/abs/2205.00445

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