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.
翻译:巨大的语言模型(LMs)为AI带来了一个新时代,成为自然语言知识任务的门户。虽然LMs是现代AI的一个基本组成部分,但LMs在很多方面也有内在的限制。我们讨论了这些局限性以及如何通过采用系统方法避免这些局限性。我们将挑战概念化为除了语言处理外还涉及知识和推理的挑战,我们定义了具有多种神经模型的灵活结构,并辅之以独立的知识和推理模块。我们描述了被称为“模块理性、知识和语言”(MRKL, 直译为“奇迹”)的神经-精神结构,以及实施该系统的一些技术挑战,以及Jurassic-X, AI21实验室的MRKL系统。