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 …

This survey article has grown out of the GAIED (pronounced" guide") workshop organized by
the authors at the NeurIPS 2023 conference. We organized the GAIED workshop as part of a …

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), while effective in integrating external knowledge to
address the limitations of large language models (LLMs), can be undermined by imperfect …

While auxiliary information has become a key to enhancing Large Language Models
(LLMs), relatively little is known about how LLMs merge these contexts, specifically contexts …

Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the
development of Large Language Models (LLMs). While much of the current research in this …

Retrieval Augmented Generation (RAG) improves large language models (LMs) by
incorporating non-parametric knowledge through evidence retrieval from external sources …

Retrieval-Augmented Generation (RAG) has emerged as a pivotal innovation in natural
language processing, enhancing generative models by incorporating external information …

Medical open-domain question answering demands substantial access to specialized
knowledge. Recent efforts have sought to decouple knowledge from model parameters …

While auxiliary information has become a key to enhance Large Language Models (LLMs),
relatively little is known about how well LLMs merge these contexts, specifically generated …