Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the
semantic web community's exploration into multi-modal dimensions unlocking new avenues …

Large language models (LLMs) have garnered unprecedented advancements across
diverse fields, ranging from natural language processing to computer vision and beyond …

In software evolution, resolving the emergent issues within GitHub repositories is a complex
challenge that involves not only the incorporation of new code but also the maintenance of …

Large language models (LLMs) have achieved impressive advancements across numerous
disciplines, yet the critical issue of knowledge conflicts, a major source of hallucinations, has …

The rapid development of large language models (LLMs) has significantly transformed the
field of artificial intelligence, demonstrating remarkable capabilities in natural language …

Alignment of pretrained LLMs using instruction-based datasets is critical for creating fine-
tuned models that reflect human preference. A growing number of alignment-based fine …

Efficient fine-tuning of large language models for task-specific applications is imperative, yet
the vast number of parameters in these models makes their training increasingly …

In this work, we aim to simultaneously enhance the effectiveness and efficiency of Mixture-of-
Experts (MoE) methods. To achieve this, we propose MoE++, a general and heterogeneous …

In this paper, we introduce Dynamic Layer Operations (DLO), a novel approach for vertically
scaling transformer-based Large Language Models (LLMs) by dynamically expanding …

Guided by the belief of the scaling law, large language models (LLMs) have achieved
impressive performance in recent years. However, scaling law only gives a qualitative …