Retrieval-augmented generation for large language models: A survey

Y Gao, Y **ong, X Gao, K Jia, J Pan, Y Bi, Y Dai… - arxiv preprint arxiv …, 2023 - arxiv.org
Large language models (LLMs) demonstrate powerful capabilities, but they still face
challenges in practical applications, such as hallucinations, slow knowledge updates, and …

A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions

L Huang, W Yu, W Ma, W Zhong, Z Feng… - ACM Transactions on …, 2024 - dl.acm.org
The emergence of large language models (LLMs) has marked a significant breakthrough in
natural language processing (NLP), fueling a paradigm shift in information acquisition …

From matching to generation: A survey on generative information retrieval

X Li, J **, Y Zhou, Y Zhang, P Zhang, Y Zhu… - arxiv preprint arxiv …, 2024 - arxiv.org
Information Retrieval (IR) systems are crucial tools for users to access information, widely
applied in scenarios like search engines, question answering, and recommendation …

Retrieval-augmented generation for ai-generated content: A survey

P Zhao, H Zhang, Q Yu, Z Wang, Y Geng, F Fu… - arxiv preprint arxiv …, 2024 - arxiv.org
The development of Artificial Intelligence Generated Content (AIGC) has been facilitated by
advancements in model algorithms, scalable foundation model architectures, and the …

Astute rag: Overcoming imperfect retrieval augmentation and knowledge conflicts for large language models

F Wang, X Wan, R Sun, J Chen, SÖ Arık - arxiv preprint arxiv:2410.07176, 2024 - arxiv.org
Retrieval-Augmented Generation (RAG), while effective in integrating external knowledge to
address the limitations of large language models (LLMs), can be undermined by imperfect …

Fine tuning vs. retrieval augmented generation for less popular knowledge

H Soudani, E Kanoulas, F Hasibi - … of the 2024 Annual International ACM …, 2024 - dl.acm.org
Language Models (LMs) memorize a vast amount of factual knowledge, exhibiting strong
performance across diverse tasks and domains. However, it has been observed that the …

Feb4rag: Evaluating federated search in the context of retrieval augmented generation

S Wang, E Khramtsova, S Zhuang… - Proceedings of the 47th …, 2024 - dl.acm.org
Federated search systems aggregate results from multiple search engines, selecting
appropriate sources to enhance result quality and align with user intent. With the increasing …

A survey of conversational search

F Mo, K Mao, Z Zhao, H Qian, H Chen, Y Cheng… - arxiv preprint arxiv …, 2024 - arxiv.org
As a cornerstone of modern information access, search engines have become
indispensable in everyday life. With the rapid advancements in AI and natural language …

How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

S Wu, J **e, J Chen, T Zhu, K Zhang, Y **ao - arxiv preprint arxiv …, 2024 - arxiv.org
By leveraging the retrieval of information from external knowledge databases, Large
Language Models (LLMs) exhibit enhanced capabilities for accomplishing many knowledge …

IR-RAG@ SIGIR24: Information retrieval's role in RAG systems

F Petroni, F Siciliano, F Silvestri… - Proceedings of the 47th …, 2024 - dl.acm.org
In recent years, Retrieval Augmented Generation (RAG) systems have emerged as a pivotal
component in the field of artificial intelligence, gaining significant attention and importance …