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Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.
Two-phase process of document retrieval using dense embeddings and LLM for answer formulation. Retrieval-augmented generation (RAG) is a two-phase process involving document retrieval and answer generation by a large language model. The initial phase uses dense embeddings to retrieve documents.
This approach is also known formally as retrieval-augmented generation [11] Though not as predominant as BERTScore, sentence embeddings are commonly used for sentence similarity evaluation which sees common use for the task of optimizing a Large language model 's generation parameters is often performed via comparing candidate sentences against ...
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. The largest and most capable LLMs are generative pretrained transformers (GPTs).
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Gen Zers, specifically women in their late 20s, have been taking to TikTok to reveal that they’re learning ballet for the first time. While taking up a hobby in adulthood isn’t anything new ...
Procedural generation – Method in which data is created algorithmically as opposed to manually; Retrieval-augmented generation – Type of information retrieval using LLMs; Stochastic parrot – Term used in machine learning