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

As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can
offer reliable and up-to-date external knowledge, providing huge convenience for numerous …

Multilingual Large Language Models are capable of using powerful Large Language
Models to handle and respond to queries in multiple languages, which achieves remarkable …

Multi-Hop Question Answering (MHQA) tasks present a significant challenge for large
language models (LLMs) due to the intensive knowledge required. Current solutions, like …

The rapid development of Large Language Models (LLMs) demonstrates remarkable
multilingual capabilities in natural language processing, attracting global attention in both …

Despite efforts to expand the knowledge of large language models (LLMs), knowledge gaps-
-missing or outdated information in LLMs--might always persist given the evolving nature of …

The robustness of recent Large Language Models (LLMs) has become increasingly crucial
as their applicability expands across various domains and real-world applications. Retrieval …

Despite Retrieval-Augmented Generation (RAG) showing promising capability in leveraging
external knowledge, a comprehensive evaluation of RAG systems is still challenging due to …

Biomedical knowledge is uniquely complex and structured, requiring distinct reasoning
strategies compared to other scientific disciplines like physics or chemistry. Biomedical …

The rise of large language models (LLMs) has significantly influenced the quality of
information in decision-making systems, leading to the prevalence of AI-generated content …