Large language models (LLMs) demonstrate powerful capabilities, but they still face
challenges in practical applications, such as hallucinations, slow knowledge updates, and …

The rise of Multimodal Large Language Models (MLLMs) has become a transformative force
in the field of artificial intelligence, enabling machines to process and generate content …

This survey provides an in-depth analysis of knowledge conflicts for large language models
(LLMs), highlighting the complex challenges they encounter when blending contextual and …

Retrieval-Augmented Generation (RAG) aims to generate more reliable and accurate
responses, by augmenting large language models (LLMs) with the external vast and …

Large language models augmented with task-relevant documents have demonstrated
impressive performance on knowledge-intensive tasks. However, regarding how to obtain …

Model editing aims to precisely alter the behaviors of large language models (LLMs) in
relation to specific knowledge, while leaving unrelated knowledge intact. This approach has …

Supervised fine-tuning (SFT) is a crucial step for large language models (LLMs), enabling
them to align with human instructions and enhance their capabilities in downstream tasks …

Knowledge editing (KE) aims to efficiently and precisely modify the behavior of large
language models (LLMs) to update specific knowledge without negatively influencing other …

Large Language Models (LLMs) have demonstrated great potential in complex reasoning
tasks, yet they fall short when tackling more sophisticated challenges, especially when …

Large language model (LLM) based agents have recently attracted much attention from the
research and industry communities. Compared with original LLMs, LLM-based agents are …