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- Retrieval-Augmented Generation for Large Language Models: A Survey
View a PDF of the paper titled Retrieval-Augmented Generation for Large Language Models: A Survey, by Yunfan Gao and 8 other authors
- [2408. 08921] Graph Retrieval-Augmented Generation: A Survey - arXiv. org
Abstract page for arXiv paper 2408 08921: Graph Retrieval-Augmented Generation: A Survey Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining
- Retrieval-Augmented Generation for Large Language Models: A Survey
Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases
- A Survey of Graph Retrieval-Augmented Generation for Customized Large . . .
This survey presents a systematic analysis of Graph-based Retrieval-Augmented Generation (GraphRAG), a new paradigm that revolutionizes domain-specific LLM applications
- A Survey on Retrieval-Augmented Text Generation for Large Language Models
As RAG grows in complexity and incorporates multiple concepts that can influence its performance, this paper organizes the RAG paradigm into four categories: pre-retrieval, retrieval, post-retrieval, and generation, offering a detailed perspective from the retrieval viewpoint
- Retrieval-Augmented Generation for Large Language Models: A Survey - ar5iv
Large Language Models (LLMs) demonstrate significant capabilities but face challenges such as hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases
- Towards Trustworthy Retrieval Augmented Generation for Large Language . . .
By integrating context retrieval into content generation, RAG provides reliable and up-to-date external knowledge, reduces hallucinations, and ensures relevant context across a wide range of tasks
- Retrieval-Augmented Generation for Large Language Models: A Survey
cally outline the entire process of Retrieval-Augmented Gen-eration (RAG) and focuses specifically on research related to augmenting the generation of large language models through knowledge retrieval The development of RAG algorithms and models is il-lustrated in Fig 1 On a timeline, most of the research re-
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