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

Large language models (LLMs) have demonstrated great success in various fields,
benefiting from their huge amount of parameters that store knowledge. However, LLMs still …

As a primary means of information acquisition, information retrieval (IR) systems, such as
search engines, have integrated themselves into our daily lives. These systems also serve …

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 …

Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of
large language models (LLMs) by incorporating external knowledge sources. This method …

Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating
up-to-date information, mitigating hallucinations, and enhancing response quality …

Dense retrieval has become a prominent method to obtain relevant context or world
knowledge in open-domain NLP tasks. When we use a learned dense retriever on a …

This survey presents an in-depth exploration of knowledge distillation (KD) techniques
within the realm of Large Language Models (LLMs), spotlighting the pivotal role of KD in …

In this work, we introduce ChatQA, a suite of models that outperform GPT-4 on retrieval-
augmented generation (RAG) and conversational question answering (QA). To enhance …

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-missing or outdated information in LLMs--might always persist given the evolving nature of …