Knowledge Tree
Relations (1)
related 2.32 — strongly supporting 4 facts
Large Language Models and Symbolic Artificial Intelligence are related through the ongoing research into neuro-symbolic integration, where LLMs are used to bridge connectionist and symbolic paradigms [1]. LLMs are increasingly viewed as a backbone for hybrid systems that combine the flexibility of language models with the structured reasoning of symbolic AI [2], [3], and [4].
Facts (4)
Sources
The Synergy of Symbolic and Connectionist AI in LLM-Empowered ... arxiv.org 1 fact
claimAdvancements in Large Language Models (LLMs) and foundation models have catalyzed the integration of connectionist and symbolic AI paradigms.
Do large language models “understand” their knowledge? aiche.onlinelibrary.wiley.com 1 fact
perspectiveV Venkatasubramanian proposes that Large Language Models should be integrated with an algebraic representation of knowledge that includes symbolic AI elements to overcome current limitations.
The Synergy of Symbolic and Connectionist AI in LLM ... arxiv.org 1 fact
claimLLM-based agents are better able to handle ambiguity and generate human-like responses compared to symbolic AI because the knowledge embedded in LLMs is more flexible.
The Integration of Symbolic and Connectionist AI in LLM-Driven ... econpapers.repec.org 1 fact
claimLarge Language Models (LLMs) exhibit traits of both symbolic and connectionist paradigms and can serve as the backbone for integrating these approaches to improve decision-making, natural language understanding, and autonomy in intelligent agents.