论文标题
MRKL系统：一种模块化的神经符号结构，结合了大型语言模型，外部知识来源和离散推理
MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning
论文作者
论文摘要
庞大的语言模型（LMS）迎来了AI的新时代，并成为通往基于自然语言的知识任务的门户。尽管LMS也在多种方式上固有地限制了现代AI的基本要素。我们讨论这些限制以及如何通过采用系统方法来避免它们。将挑战概念化为涉及知识和推理的挑战，除了语言处理外，我们还定义了具有多种神经模型的灵活体系结构，并由离散的知识和推理模块互补。我们描述了这种神经符号结构，称为模块化推理，知识和语言（MRKL，发音为“奇迹”）系统，以及实施IT的一些技术挑战，而AI21 Labs的MRKL系统实现了侏罗纪X。
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.
