retrieval augmented generative

We recently made substantial progress in this realm with our Retrieval Augmented Generation (RAG) architecture, an end-to-end differentiable model that combines an information retrieval component (Facebook AI’s dense-passage retrieval system)…

Summary Huggingface Transformers recently added the Retrieval Augmented Generation (RAG) model, a new NLP architecture that leverages external documents (like Wikipedia) to augment its knowledge and achieve state of the art results on knowledge-intensive tasks.

Retrieval-Augmented Generative Question Answering for Event Argument Extraction. Xinya Du, Heng Ji. Event argument extraction has long been studied as a sequential prediction problem with extractive-based methods, tackling each argument…

Recently, retrieval-augmented text generation attracted increasing attention of the computational linguistics community. Compared with conventional generation models, retrieval-augmented text generation has remarkable advantages and particularly has achieved state-of-the-art performance in…

for retrieval - augmented generation (RAG) — models which combine pre-trained parametric and non-parametric memory for language generation. We introduce RAG models where the parametric memory is a pre-trained seq2seq model and…

Tutorial: Generative QA with Retrieval-Augmented Generation. Open in Colab Download. While extractive QA highlights the span of text that answers a query, generative QA can return a novel text answer…

Key Features • Installation • Components • Examples • How To Use • Benchmarks. fastRAG is a research framework designed to facilitate the building of retrieval augmented generative pipelines. Its…

Retrieval Augmented Generation (RAG) - YouTube In this video, we discuss RAG( Retrieval augmented generation). In RAG formulation, we use a retriever to retrieve relevant passages and use a...

In “REALM: Retrieval - Augmented Language Model Pre-Training”, accepted at the 2020 International Conference on Machine Learning, we share a novel paradigm for language model pre-training, which augments a language representation model…

In summary, we present a simple framework for retrieval-augmented generative modeling with diffusion models. By searching in and conditioning on the latent space of CLIP Radford et al. (2021), we…

In summary, we present a simple framework for retrieval-augmented generative modeling with diffusion models. By searching and conditioning on the latent space of CLIP Radford et al. (2021), we present…