Facebook, AI “RAG” brings innovation in NLP models


Natural Language Processing (NLP) is the process of training computers to comprehend the ways how Human beings speak and write. The approach has seen a tremendous change over a couple of years and today we can see general-purpose language models are meticulously used for a variety of distinct tasks. One fine example could be Sentiment Analysis which enables a human to produce the solution despite the absence of background knowledge.

It is indeed challenging to build a model that researches and contextualizes. As a ray of hope, an end-to-end differentiable model namely “Retrieval Augmented Generation” RAG was brought in. Substantial progress has been made in this RAG architecture, as it combines an Information retrieval component and Seq2Seq generator. The information component takes acre by Facebook AI’s dense passage retrieval system and the generator part by the BART model.

Facebook in collaboration with Hugging Face has come up with Retrieval Augmented Generation (RAG), an open-source Natural Language Processing model. RAG makes use of NLP models to obtain up-to-date information and corresponding outputs are generated with the help of the seq2seq generator. Thus RAG assists in both research and Contextual part by itself.

An intriguing thing about RAG is that it provides solutions in an easier way. To be more precise, a five-line code is more enough for RAG to bring out the appropriate solution. Thus allowing Researchers and Engineers to develop and deploy appropriate solutions at a greater pace. RAG has been made available at the Hugging Face Transformer library. The newly added component to the library thus able to provide its indexed knowledge source. Input data is used to retrieve relevant documents from databases. For example, a set of documents can be retrieved from the Wikipedia database.

RAG predicts solution on the first hand prior to the release of final prediction scores. It uses “Late Fusion” in order to integrate knowledge obtained from the retrieved sources. The performance can be enhanced further when the access to documents such as Clues relevant to the solution is permitted. It is interesting to know that it excels at NLP queries explored by Facebook.


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