Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …

The emergence of large-scale pre-trained language models, such as ChatGPT, has
revolutionized various research fields in artificial intelligence. Transformersbased large …

The recent" pre-train, prompt, predict training" paradigm has gained popularity as a way to
learn generalizable models with limited labeled data. The approach involves using a pre …

Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …

Background Prompt engineering, focusing on crafting effective prompts to large language
models (LLMs), has garnered attention for its capabilities at harnessing the potential of …

The emergence of large-scale pre-trained language models has revolutionized various AI
research domains. Transformers-based Large Language Models (LLMs) have gradually …

Recent research tends to address the biomedical entity linking problem in a unified
framework solely based on surface form matching between mentions and entities …

Prompt learning has demonstrated impressive efficacy in the fine-tuning of multimodal large
models to a wide range of downstream tasks. Nonetheless, applying existing prompt …

Multi-label intent detection aims to recognize multiple user intents behind dialogue
utterances. The diversity of user utterances and the scarcity of training data motivate multi …

Graph neural networks (GNNs) are widely applied in graph data modeling. However,
existing GNNs are often trained in a task-driven manner that fails to fully capture the intrinsic …