Large Language Models (LLMs) have shown extraordinary capabilities in understanding
and generating text that closely mirrors human communication. However, a primary …

Despite the ability to train capable LLMs, the methodology for maintaining their relevancy
and rectifying errors remains elusive. To this end, the past few years have witnessed a surge …

Nowadays, real-world applications often face streaming data, which requires the learning
system to absorb new knowledge as data evolves. Continual Learning (CL) aims to achieve …

Chain-of-Thought (CoT) prompting has been shown to enhance the multi-step reasoning
capabilities of Large Language Models (LLMs). However, debates persist about whether …

Evaluation is the baton for the development of large language models. Current evaluations
typically employ a single-item assessment paradigm for each atomic test objective, which …

The leakage of benchmark data into the training data has emerged as a significant
challenge for evaluating the capabilities of large language models (LLMs). In this work, we …

Even with their impressive abilities, Large Language Models (LLMs) such as ChatGPT are
not immune to issues of factual or logically consistent. Concretely, the key concern is how to …

Continual learning (CL) is crucial for evaluating adaptability in learning solutions to retain
knowledge. Our research addresses the challenge of catastrophic forgetting, where models …

In general class-incremental learning, researchers typically use sample sets as a tool to
avoid catastrophic forgetting during continuous learning. At the same time, researchers have …

As artificial intelligence progresses in the field of medical ultrasound image analysis,
mitigating catastrophic forgetting during continuous learning processes in disease diagnosis …