Recently, foundation language models (LMs) have marked significant achievements in the
domains of natural language processing (NLP) and computer vision (CV). Unlike traditional …

In this work, we present HiReview, a novel framework for hierarchical taxonomy-driven
automatic literature review generation. With the exponential growth of academic documents …

During the pretraining phase, large language models (LLMs) acquire vast amounts of
knowledge from extensive text corpora. Nevertheless, in later stages such as fine-tuning and …

Large language models (LLMs) have shown significant achievements in solving a wide
range of tasks. Recently, LLMs' capability to store, retrieve and infer with symbolic …

Continual learning (CL) aims to empower machine learning models to learn continually from
new data, while building upon previously acquired knowledge without forgetting. As …

Large Language Models (LLMs) need to adapt to the continuous changes in data, tasks, and
user preferences. Due to their massive size and the high costs associated with training …

Lifelong learning, also known as continual or incremental learning, is a crucial component
for advancing Artificial General Intelligence (AGI) by enabling systems to continuously adapt …

We argue that advancing LLM-based human simulation requires addressing both LLM's
inherent limitations and simulation framework design challenges. Recent studies have …

Although substantial efforts have been made to mitigate catastrophic forgetting in continual
learning, the intrinsic mechanisms are not well understood. In this paper, we discover that …

Large language models (LLMs) have seen staggering progress in recent years.
Contemporary LLMs rely on an immense amount of data for training, however, as LLMs …