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Jun 11, 2024 · Retrieval-augmented generation (RAG) is an innovative approach in the field of natural language processing (NLP) that combines the strengths of retrieval-based and generation-based models to enhance the quality of generated text.
Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response.
Aug 22, 2023 · Retrieval-augmented generation (RAG) is an AI framework for improving the quality of LLM-generated responses by grounding the model on external sources of knowledge to supplement the LLM’s internal representation of information.
Nov 15, 2023 · Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources.
May 22, 2020 · We explore a general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) -- models which combine pre-trained parametric and non-parametric memory for language generation.
Feb 29, 2024 · Retrieval-Augmented Generation for AI-Generated Content: A Survey. Advancements in model algorithms, the growth of foundational models, and access to high-quality datasets have propelled the evolution of Artificial Intelligence Generated Content (AIGC).
Retrieval augmented generation ( RAG) is a type of information retrieval process. It modifies interactions with a large language model (LLM) so that it responds to queries with reference to a specified set of documents, using it in preference to information drawn from its own vast, static training data.
Sep 21, 2023 · Retrieval-augmented generation (RAG) is an advanced artificial intelligence (AI) technique that combines information retrieval with text generation, allowing AI models to retrieve relevant information from a knowledge source and incorporate it into generated text.
Jun 11, 2024 · Retrieval Augmented Generation (RAG) signifies a transformative advancement in large language models (LLMs). It combines the generative prowess of transformer architectures with dynamic information retrieval.
Retrieval-Augmented Generation (RAG) has recently emerged as a paradigm to address such challenges. In partic-ular, RAG introduces the information retrieval process, which enhances the generation process by retrieving relevant objects from available data stores, leading to higher accuracy and better robustness.
