Overview of Pretrained Language Models in NLP
In recent years, pre-trained language models like BERT, GPT-3, and RoBERTa have revolutionized Natural Language Processing (NLP). These models, trained on vast text corpora, have demonstrated remarkable capabilities in text generation, translation, and comprehension tasks. However, they have inherent limitations:
- Memory Constraints: Pre-trained models store information within their parameters, which limits their ability to recall specific facts or handle out-of-distribution queries.
- Scalability Issues: As the need for storing more information grows, the size of the models must increase, leading to inefficiencies in computation and deployment.
- Static Knowledge: Once trained, these models cannot dynamically update their knowledge without retraining, making them less adaptable to new information.
To address these limitations, researchers have introduced Retrieval-Augmented Generation (RAG) models.
Retrieval-Augmented Generation (RAG) for Knowledge-Intensive NLP Tasks
Natural language processing (NLP) has undergone a revolution thanks to trained language models, which achieve cutting-edge results on various tasks. Even still, these models often fail in knowledge-intensive jobs requiring reasoning over explicit facts and textual material, despite their excellent skills.
Researchers have developed a novel strategy known as Retrieval-Augmented Generation (RAG) to get around this restriction. In this article, we will explore the limitations of pre-trained models and learn about the RAG model and its configuration, training, and decoding methodologies.
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