Concept-Oriented Deep Learning with Large Language Models
- URL: http://arxiv.org/abs/2306.17089v2
- Date: Tue, 19 Sep 2023 21:15:52 GMT
- Title: Concept-Oriented Deep Learning with Large Language Models
- Authors: Daniel T. Chang
- Abstract summary: 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)
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.
- Score: 0.4548998901594072
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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