Existing studies on meta-learning based few-shot fault diagnosis largely focus on constant
speed scenarios, neglecting the consideration of more realistic scenarios involving unstable …

This paper presents an exhaustive quantitative and qualitative evaluation of Large
Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning. We …

Large-scale pre-trained language models have contributed significantly to natural language
processing by demonstrating remarkable abilities as few-shot learners. However, their …

Event extraction requires high-quality expert human annotations, which are usually
expensive. Therefore, learning a data-efficient event extraction model that can be trained …

The goal of meta-learning is to learn to adapt to a new task with only a few labeled
examples. To tackle this problem in NLP, we propose $\textit {in-context tuning} $, which …

We introduce ClusterLLM, a novel text clustering framework that leverages feedback from an
instruction-tuned large language model, such as ChatGPT. Compared with traditional …

Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such
as social network analysis, financial fraud detection, and drug discovery. As a central …

Event extraction (EE) is a crucial research task for promptly apprehending event information
from massive textual data. With the rapid development of deep learning, EE based on deep …

Few-shot Learning (FSL) is aimed to make predictions based on a limited number of
samples. Structured data such as knowledge graphs and ontology libraries has been …

Supervised machine learning has several drawbacks that make it difficult to use in many
situations. Drawbacks include heavy reliance on massive training data, limited …