Causal inference has shown potential in enhancing the predictive accuracy, fairness,
robustness, and explainability of Natural Language Processing (NLP) models by capturing …

Causal reasoning (CR) is a crucial aspect of intelligence, essential for problem-solving,
decision-making, and understanding the world. While large language models (LLMs) can …

Recent breakthroughs in artificial intelligence have driven a paradigm shift, where large
language models (LLMs) with billions or trillions of parameters are trained on vast datasets …

Class-Incremental Learning (CIL) is a practical and challenging problem for achieving
general artificial intelligence. Recently, Pre-Trained Models (PTMs) have led to …

The evolution of communication technology has driven the demand for intelligent power
grids and data analysis in power systems. However, obtaining and annotating electrical data …

Existing machine reading comprehension methods use a fixed stride to chunk long texts,
which leads to missing contextual information at the boundaries of the chunks and a lack of …

As the applications of large language models (LLMs) expand across diverse fields, the
ability of these models to adapt to ongoing changes in data, tasks, and user preferences …

Commonsense question answering (CSQA) is a challenging task in the field of knowledge
graph question answering. It combines the context of the question with the relevant …

With the advancement of carbon emissions reduction initiatives, there is a rapid increase in
the current demand for electrical power, which necessitates a more efficient and reliable grid …

Class-Incremental Learning (CIL) is a practical and challenging problem for achieving
general artificial intelligence. Pre-Trained Models (PTMs) have recently led to breakthroughs …