Knowledge Tree
Relations (1)
related 2.58 — strongly supporting 5 facts
Large Language Models are fundamentally linked to few-shot learning as they possess this as an emergent capability [1], [2], and are explicitly defined as few-shot learners in foundational research [3]. Furthermore, few-shot learning is a core technique integrated with Large Language Models to enhance tasks such as knowledge graph construction [4], [5].
Facts (5)
Sources
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 2 facts
claimLarge language models are versatile across tasks like text generation and summarization, possess strong contextual understanding, are scalable, and demonstrate zero-shot and few-shot learning capabilities.
referenceThe paper 'Leveraging LLMs few-shot learning to improve instruction-driven knowledge graph construction' by Mou, Y., Liu, L., Sowe, S., Collarana, D., Decker, S. explores using few-shot learning with large language models to improve instruction-driven knowledge graph construction.
The construction and refined extraction techniques of knowledge ... nature.com 1 fact
claimThe knowledge graph construction framework incorporates a collaborative mechanism with Large Language Models (LLMs), combining domain LLMs and deep learning technologies with few-shot learning and transfer learning to extract domain knowledge from unstructured data.
A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com 1 fact
claimLarge Language Models (LLMs) possess emergent capabilities such as zero-shot learning (performing tasks without examples) and few-shot learning (solving new tasks with few examples).
A Survey on the Theory and Mechanism of Large Language Models arxiv.org 1 fact
claimThe paper 'Language models are few-shot learners' (Brown et al., Advances in neural information processing systems 33) establishes the foundational capability of large language models to perform tasks with few-shot learning.