RAG: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Автор: Brahmagupta
Загружено: 2026-02-14
Просмотров: 17
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Paper: https://arxiv.org/abs/2312.10997
The provided research paper introduces Retrieval-Augmented Generation (RAG), an innovative framework that enhances pre-trained language models by combining parametric and non-parametric memory. While standard models rely solely on their internal weights, RAG utilises a neural retriever to access an external document index, such as Wikipedia, to provide factual context during text generation. This architecture effectively addresses common limitations in AI, such as the tendency to hallucinate or the difficulty of updating stored knowledge without retraining. By evaluating the system on knowledge-intensive tasks like open-domain question answering and fact verification, the authors demonstrate that RAG achieves superior accuracy and generates more diverse, specific, and factual responses. Furthermore, the researchers highlight the model's flexibility through index hot-swapping, allowing the system’s world knowledge to be updated instantly by simply replacing the external data source.
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