Large Language Models (LLMs) have been successfully used in many natural-language tasks and applications including text generation and AI chatbots. They also are a promising new technology for concept-oriented deep learning (CODL). However, the prerequisite is that LLMs understand concepts and ensure conceptual consistency. We discuss these in this paper, as well as major uses of LLMs for CODL including concept extraction from text, concept graph extraction from text, and concept learning. Human knowledge consists of both symbolic (conceptual) knowledge and embodied (sensory) knowledge. Text-only LLMs, however, can represent only symbolic (conceptual) knowledge. Multimodal LLMs, on the other hand, are capable of representing the full range (conceptual and sensory) of human knowledge. We discuss conceptual understanding in visual-language LLMs, the most important multimodal LLMs, and major uses of them for CODL including concept extraction from image, concept graph extraction from image, and concept learning. While uses of LLMs for CODL are valuable standalone, they are particularly valuable as part of LLM applications such as AI chatbots.

翻译：大语言模型（LLMs）已成功应用于包括文本生成和AI聊天机器人在内的众多自然语言任务与应用中。它们也是一种有望实现面向概念深度学习（CODL）的新兴技术。然而，前提条件是LLMs需理解概念并确保概念一致性。本文讨论了这些问题，以及LLMs在CODL中的主要用途，包括从文本中提取概念、从文本中提取概念图以及概念学习。人类知识既包含符号（概念性）知识，也包含具身（感知性）知识。然而，纯文本LLMs只能表征符号（概念性）知识。相比之下，多模态LLMs能够表征人类知识的全范围（概念性与感知性）。我们探讨了视觉-语言LLMs（最重要的多模态LLMs）中的概念理解，以及它们在CODL中的主要用途，包括从图像中提取概念、从图像中提取概念图以及概念学习。尽管LLMs在CODL中的独立应用具有价值，但作为AI聊天机器人等LLM应用的一部分，其价值尤为突出。