RAG: Retrieval-Augmented Generation

https://youtu.be/T-D1OfcDW1M?si=6yp9Y3trSjVDiYpX

 In the context of large language models (LLMs), "rag" or "RAG" typically refers to "Retrieval-Augmented Generation." RAG is a technique that combines traditional language modeling with information retrieval to improve the performance and accuracy of LLMs in various natural language processing tasks, such as question answering, document summarization, and dialogue systems.

Key components and concepts of RAG in LLMs:

1. Retrieval: RAG models use an information retrieval system to find relevant documents or passages from a large external knowledge base based on the input query or context.

2. Augmentation: The retrieved information is then used to augment the input to the language model, providing additional context to help generate more accurate and informative responses.

3. Generation: The augmented input is passed through the language model, which generates the final output based on both the original input and the retrieved information.

4. Knowledge base: RAG models rely on a large external knowledge base, such as Wikipedia or custom-built domain-specific databases, to retrieve relevant information for augmenting the input.

5. Improved performance: By incorporating retrieved information, RAG models can generate more accurate, specific, and factually grounded responses compared to traditional language models that rely solely on their training data.

Some notable examples of RAG models include Dense Passage Retrieval (DPR) and RAG-Token, both developed by Facebook AI Research. These models have achieved state-of-the-art performance on various question-answering benchmarks, showcasing the effectiveness of the retrieval-augmented generation approach in LLMs.​​​​​​​​​​​​​​​​

在大型語言模型（LLMs）的背景下，「rag」或「RAG」通常指的是「檢索增強生成」（Retrieval-Augmented Generation）。RAG 是一種結合傳統語言建模和資訊檢索的技術，用於改善 LLMs 在各種自然語言處理任務中的性能和準確性，例如問答、文件摘要和對話系統。

RAG 在 LLMs 中的關鍵組成部分和概念：

1. 檢索：RAG 模型使用資訊檢索系統根據輸入查詢或上下文從大型外部知識庫中找出相關的文件或段落。

2. 增強：檢索到的資訊然後用於增強語言模型的輸入，提供額外的上下文以幫助生成更準確和資訊豐富的回應。

3. 生成：增強的輸入被傳遞到語言模型中，語言模型根據原始輸入和檢索到的資訊生成最終輸出。

4. 知識庫：RAG 模型依賴於大型外部知識庫，例如維基百科或自定義建立的特定領域資料庫，以檢索相關資訊來增強輸入。

5. 改善性能：通過結合檢索到的資訊，與僅依賴其訓練資料的傳統語言模型相比，RAG 模型可以生成更準確、具體和事實基礎的回應。

一些著名的 RAG 模型包括 Facebook AI Research 開發的 Dense Passage Retrieval（DPR）和 RAG-Token。這些模型在各種問答基準測試中達到了最先進的性能，展示了檢索增強生成方法在 LLMs 中的有效性。​​​​​​​​​​​​​​​​