Starting from the resurgence of deep learning, vision-language models (VLMs) benefiting
from large language models (LLMs) have never been so popular. However, while LLMs can …

Existing approaches on zero-shot event detection usually train models on datasets
annotated with known event types, and prompt them with unseen event definitions. These …

In this paper, we propose a selfdistillation framework with meta learning (MetaSD) for
knowledge graph completion with dynamic pruning, which aims to learn compressed graph …

When applying deep neural network language models to related systems (eg, question
answering systems, chatbots, and intelligent assistants), many datasets contain different …

Large-scale multilingual Pretrained Language Models (mPLMs) yield impressive
performance on cross-language tasks, yet significant performance disparities exist across …

Training LLMs on data that contains unfamiliar knowledge during the instruction tuning
stage can make LLMs overconfident and encourage hallucinations. To address this …

Distantly-Supervised Named Entity Recognition (DS-NER) is widely used in real-world
scenarios. It can effectively alleviate the burden of annotation by matching entities in existing …