This section provides a detailed introduction to the core mechanism of the RAG model, its application scenarios, as well as its advantages and limitations in generation tasks.
With the rapid development of Natural Language Processing (NLP) technology, generative language models (such as GPT, BART, etc.) have demonstrated excellent performance in various text generation tasks, especially in language generation and context understanding. However, pure generative models have some inherent limitations when dealing with factual tasks. For example, since these models rely on fixed pre-trained data, they may "fabricate" information when answering questions that require the latest or real-time information, resulting in inaccurate or factually unfounded generated results. In addition, when faced with long-tail questions and complex reasoning tasks, generative models often perform poorly due to the lack of external knowledge support in specific domains, and it is difficult for them to provide sufficient depth and accuracy.
At the same time, the retrieval model (Retriever) can solve the problem of factual queries by quickly finding relevant information in a large number of documents. However, traditional retrieval models (such as BM25) often can only return isolated results when faced with fuzzy queries or cross-domain problems, and are unable to generate coherent natural language answers. Due to the lack of context reasoning ability, the answers generated by the retrieval model are usually not coherent and complete enough.
In order to address the deficiencies of these two types of models, the Retrieval-Augmented Generation (RAG) model came into being. By combining the advantages of generative models and retrieval models, RAG obtains relevant information from an external knowledge base in real time and integrates it into the generation task, ensuring that the generated text not only has context coherence but also contains accurate knowledge. This hybrid architecture performs particularly well in scenarios such as intelligent question answering, information retrieval and reasoning, and domain-specific content generation.
- The Definition of RAG
RAG is a hybrid architecture that combines information retrieval with generative models. First, the retriever obtains content fragments related to the user's query from an external knowledge base or document collection; then, the generator generates natural language output based on these retrieved contents, ensuring that the generated content is not only informative but also highly relevant and accurate.