Knowledge Tree
few-shot learning
Facts (20)
Sources
KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org May 20, 2024 4 facts
procedureThe CoE (Chain of Exploration) method begins by using a few-shot learning prompt combined with a user query to guide a planner in creating a strategic exploration plan across a knowledge graph.
claimThe language model used for triple extraction (LM_ext) is trained through a few-shot learning approach, typically involving 5-10 examples.
claimThe language model used for triple extraction (LM_ext) is trained through a few-shot learning approach, typically involving 5-10 examples.
procedureThe CoE (Chain of Exploration) method begins by using a few-shot learning prompt combined with a user query to guide a planner in creating a strategic exploration plan across a knowledge graph.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 4 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.
claimThe paper 'Ontology-enhanced prompt-tuning for few-shot learning
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.
referenceCurrent research addresses the gap between temporal knowledge graphs and large language models through retrieval-augmented generation frameworks, such as GenTKG (Liao et al., 2024), and by integrating few-shot learning and instruction tuning to reduce computational costs.
A Survey on the Theory and Mechanism of Large Language Models arxiv.org Mar 12, 2026 4 facts
referenceThe paper 'Task contamination: language models may not be few-shot anymore' argues that data contamination may invalidate the few-shot learning capabilities of language models.
claimLi and Flanigan (2024) found that a model's superior performance in zero- or few-shot settings may stem from exposure to task-related samples during pre-training rather than genuine generalization.
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.
claimIn-context learning is a form of few-shot learning where a model is provided with a small number of input-label pairs as examples, allowing the model to recognize a task and provide an answer for a query without parameter updates.
Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org Feb 16, 2025 3 facts
claimTransfer learning, which includes pre-training, fine-tuning, and few-shot learning, allows AI models to efficiently adapt knowledge from one task to another.
referenceArchit Parnami and Minwoo Lee published 'Learning from few examples: A summary of approaches to few-shot learning' as an arXiv preprint in 2022.
claimThe Symbolic[Neuro] approach utilizes neural networks for context-aware predictions, such as in-context learning, few-shot learning, and Chain-of-Thought (CoT) reasoning, while employing symbolic systems to facilitate higher-order reasoning.
The construction and refined extraction techniques of knowledge ... nature.com Feb 10, 2026 2 facts
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.
procedureTo ensure the reliability and professionalism of LLM-based extraction results, the research process relies on annotations and sample verification by domain experts, which the LLM then uses to perform knowledge extraction via few-shot learning.
Combining large language models with enterprise knowledge graphs frontiersin.org Aug 26, 2024 1 fact
procedureEarly distant supervision approaches to relation extraction use supervised methods to align positive and negative pair relations for pre-training language models, followed by few-shot learning to extract relations.
A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Nov 4, 2024 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).
Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org 1 fact
referenceHoloDetect is a few-shot learning system for error detection developed by A. Heidari, J. McGrath, I.F. Ilyas, and T. Rekatsinas, presented at the 2019 SIGMOD Conference.