Large Language Models (LLMs) are swiftly advancing in architecture and capability, and as
they integrate more deeply into complex systems, the urgency to scrutinize their security …

Background Artificial intelligence (AI) is influencing our society on many levels and has
broad implications for the future practice of hematology and oncology. However, for many …

Despite the fact that large-scale Language Models (LLM) have achieved SOTA
performances on a variety of NLP tasks, its performance on NER is still significantly below …

Tabular data is one of the most commonly used types of data in machine learning. Despite
recent advances in neural nets (NNs) for tabular data, there is still an active discussion on …

With the rapid development of online services and web applications, recommender systems
(RS) have become increasingly indispensable for mitigating information overload and …

Sequential recommendation problems have received increasing attention in research during
the past few years, leading to the inception of a large variety of algorithmic approaches. In …

Large language models (LLMs) have been shown to have significant potential in few-shot
learning across various fields, even with minimal training data. However, their ability to …

Large language models have shown tremendous performance in a variety of tasks. In-
context learning--the ability to improve at a task after being provided with a number of …

Foundation large language models (LLMs) have shown an impressive ability to solve tasks
across a wide range of fields including health. To effectively solve personalized health tasks …

Recently, the topic of table pre-training has attracted considerable research interest.
However, how to employ table pre-training to boost the performance of tabular prediction …