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Retrieval-augmented generation for knowledge-intensive NLP tasks . . . We fine-tune and evaluate our models on a wide range of knowledge-intensive NLP tasks and set the state of the art on three open domain QA tasks, outperforming parametric seq2seq models and task-specific retrieve-and-extract architectures
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks We fine-tune and evaluate our models on a wide range of knowledge-intensive NLP tasks and set the state of the art on three open domain QA tasks, outperforming parametric seq2seq models and task-specific retrieve-and-extract architectures
Retrieval-Augmented Generation (RAG) for Knowledge-Intensive NLP Tasks Researchers have developed a novel strategy known as Retrieval-Augmented Generation (RAG) to get around this restriction In this article, we will explore the limitations of pre-trained models and learn about the RAG model and its configuration, training, and decoding methodologies
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks Hybrid models that combine parametric memory with non-parametric (i e retrieval-based) memories may address these issues RAG treats the retrieved document as a latent variable and proposes two models to marginalize over the latent documents in diferent ways to produce a distribution over generated text
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks In response, Retrieval-Augmented Generation (RAG) has emerged as an innovative paradigm that integrates retrieval mechanisms with generative models to enhance their overall performance RAG combines the retrieval efficiency of knowledge-based systems with the flexibility of deep generative models
Retrieval-augmented generation - Wikipedia Retrieval-augmented generation (RAG) is a technique that enables large language models (LLMs) to retrieve and incorporate new information [1] With RAG, LLMs do not respond to user queries until they refer to a specified set of documents