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8 de may. de 2024 · Retrieval-augmented generation (RAG) is a technique that combines information retrieval with a set of carefully designed system prompts to provide more accurate, up-to-date, and contextually relevant responses from large language models (LLMs).
Hace 1 día · Retrieval-Augmented Generation (RAG) offers a cost-effective approach to injecting real-time knowledge into large language models (LLMs). Nevertheless, constructing and validating high-quality knowledge repositories require considerable effort. We propose a pre-retrieval framework named Pseudo-Graph Retrieval-Augmented Generation (PG-RAG), which conceptualizes LLMs as students by providing ...
Hace 5 días · Retrieval practice is a teaching and learning strategy underpinned by cognitive science. It is a well-researched method that helps improve students’ knowledge retention of new information and recall of previously learned content. This article offers practical advice for primary and secondary teachers to implement retrieval practice in class.
Hace 5 días · This paper introduces xRAG, an innovative context compression method tailored for retrieval-augmented generation. xRAG reinterprets document embeddings in dense retrieval--traditionally used solely for retrieval--as features from the retrieval modality.
Hace 4 días · Retrieval Augmented Generation (RAG) systems are revolutionizing AI by enhancing pre-trained language models (LLMs) with external knowledge. Leveraging vector databases, organizations are crafting ...
20 de may. de 2024 · Question-Based Retrieval using Atomic Units for Enterprise RAG. Enterprise retrieval augmented generation (RAG) offers a highly flexible framework for combining powerful large language models (LLMs) with internal, possibly temporally changing, documents. In RAG, documents are first chunked.
1 de may. de 2024 · Their more manageable size makes them perfect for many applications, particularly in areas like Retrieval-Augmented Generation (RAG), where the focus leans more towards the retrieval aspect than on generation. In this post, we will explore how to implement RAG using Llama-3 and Langchain.
