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

• URL: http://arxiv.org/abs/2205.00445v1
• Date: Sun, 1 May 2022 11:01:28 GMT
• Title: MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning
• Authors: 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
• Abstract summary: Huge language models (LMs) have ushered in a new era for AI, serving as a gateway to natural-language-based knowledge tasks. 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) system.
• Score: 50.40151403246205
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Neurosymbolic Graph Enrichment for Grounded World Models [47.92947508449361]
  We present a novel approach to enhance and exploit LLM reactive capability to address complex problems. We create a multimodal, knowledge-augmented formal representation of meaning that combines the strengths of large language models with structured semantic representations. By bridging the gap between unstructured language models and formal semantic structures, our method opens new avenues for tackling intricate problems in natural language understanding and reasoning.
  arXiv  Detail & Related papers  (2024-11-19T17:23:55Z)
• Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making [51.737762570776006]
  LLM-ACTR is a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making. Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations. Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability.
  arXiv  Detail & Related papers  (2024-08-17T11:49:53Z)
• Multi-step Inference over Unstructured Data [2.169874047093392]
  High-stakes decision-making tasks in fields such as medical, legal and finance require a level of precision, comprehensiveness, and logical consistency. We have developed a neuro-symbolic AI platform to tackle these problems. The platform integrates fine-tuned LLMs for knowledge extraction and alignment with a robust symbolic reasoning engine.
  arXiv  Detail & Related papers  (2024-06-26T00:00:45Z)
• Cognition is All You Need -- The Next Layer of AI Above Large Language Models [0.0]
  We present Cognitive AI, a framework for neurosymbolic cognition outside of large language models. We propose that Cognitive AI is a necessary precursor for the evolution of the forms of AI, such as AGI, and specifically claim that AGI cannot be achieved by probabilistic approaches on their own. We conclude with a discussion of the implications for large language models, adoption cycles in AI, and commercial Cognitive AI development.
  arXiv  Detail & Related papers  (2024-03-04T16:11:57Z)
• LB-KBQA: Large-language-model and BERT based Knowledge-Based Question and Answering System [7.626368876843794]
  We propose a novel KBQA system based on a Large Language Model(LLM) and BERT (LB-KBQA) With the help of generative AI, our proposed method could detect newly appeared intent and acquire new knowledge. In experiments on financial domain question answering, our model has demonstrated superior effectiveness.
  arXiv  Detail & Related papers  (2024-02-05T16:47:17Z)
• Large Language Models for Scientific Synthesis, Inference and Explanation [56.41963802804953]
  We show how large language models can perform scientific synthesis, inference, and explanation. We show that the large language model can augment this "knowledge" by synthesizing from the scientific literature. This approach has the further advantage that the large language model can explain the machine learning system's predictions.
  arXiv  Detail & Related papers  (2023-10-12T02:17:59Z)
• Large Language Models for Information Retrieval: A Survey [58.30439850203101]
  Information retrieval has evolved from term-based methods to its integration with advanced neural models. Recent research has sought to leverage large language models (LLMs) to improve IR systems. We delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers.
  arXiv  Detail & Related papers  (2023-08-14T12:47:22Z)
• Neurosymbolic AI for Situated Language Understanding [13.249453757295083]
  We argue that computational situated grounding provides a solution to some of these learning challenges. Our model reincorporates some ideas of classic AI into a framework of neurosymbolic intelligence. We discuss how situated grounding provides diverse data and multiple levels of modeling for a variety of AI learning challenges.
  arXiv  Detail & Related papers  (2020-12-05T05:03:28Z)
• Distributed and Democratized Learning: Philosophy and Research Challenges [80.39805582015133]
  We propose a novel design philosophy called democratized learning (Dem-AI) Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently. We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
  arXiv  Detail & Related papers  (2020-03-18T08:45:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.