Recent advancements in large language models (LLMs) have significantly boosted the rise
of Role-Playing Language Agents (RPLAs), ie, specialized AI systems designed to simulate …

While textual information significantly enhances the performance of pre-trained language
models (PLMs) in knowledge graph completion (KGC), the static and noisy nature of existing …

Recent advancements in large language models (LLMs) have achieved promising
performances across various applications. Nonetheless, the ongoing challenge of …

Large Language Models (LLMs) have shown their ability to collaborate effectively with
humans in real-world scenarios. However, LLMs are apt to generate hallucinations, ie …

Large language models (LLMs) have demonstrated impressive performance and spurred
numerous AI applications, in which role-playing agents (RPAs) are particularly popular …

Aligned Large Language Models (LLMs) showcase remarkable versatility, capable of
handling diverse real-world tasks. Meanwhile, aligned LLMs are also expected to exhibit …

Knowledge tracing (KT) plays a crucial role in predicting students' future performance by
analyzing their historical learning processes. Deep neural networks (DNNs) have shown …

Knowledge tracing (KT) aims to estimate student's knowledge mastery based on their
historical interactions. Recently, the deep learning based KT (DLKT) approaches have …

The multimodal deep neural networks, represented by CLIP, have generated rich
downstream applications owing to their excellent performance, thus making understanding …

While multi-agent systems have been shown to significantly enhance the performance of
Large Language Models (LLMs) across various tasks and applications, the dense …