Speculative RAG By Google Research

🌈 Abstract

The article discusses retrieval augmented generation (RAG), which combines the generative abilities of large language models (LLMs) with external knowledge sources to provide more accurate and up-to-date responses. It covers different approaches to RAG, including:

• Standard RAG: Incorporates all documents into the prompt, with variations using summarization, re-ranking, and user feedback.
• Self-Reflective RAG: Requires specialized instruction-tuning of the language model to generate specific tags for self-reflection.
• Corrective RAG: Uses an external retrieval evaluator to refine document quality, focusing on contextual information without enhancing reasoning capabilities.
• Speculative RAG: Leverages a larger generalist LM to efficiently verify multiple RAG drafts produced in parallel by a smaller, specialized LM, each based on a distinct subset of retrieved documents.

🙋 Q&A

[01] Speculative RAG

1. What is the key idea behind Speculative RAG?

• Speculative RAG uses a smaller specialist RAG drafter to generate high-quality draft answers, with each draft coming from a distinct subset of retrieved documents.
• The larger generalist language model verifies and integrates the most promising draft into the final answer, enhancing comprehension of each subset and mitigating the lost-in-the-middle phenomenon.
• This method significantly accelerates the RAG process by having the smaller specialist LM handle drafting, while the larger generalist LM performs a single, unbiased verification pass over the drafts in parallel.

2. What are the benefits of Speculative RAG?

• Speculative RAG achieves state-of-the-art performance with reduced latency.
• It improves accuracy by up to 12.97% and reduces latency by 51% compared to traditional RAG systems.

[02] Other RAG Approaches

1. What are the key characteristics of the other RAG approaches mentioned?

• Standard RAG: Incorporates all documents into the prompt, with variations using summarization, re-ranking, and user feedback.
• Self-Reflective RAG: Requires specialized instruction-tuning of the language model to generate specific tags for self-reflection.
• Corrective RAG: Uses an external retrieval evaluator to refine document quality, focusing on contextual information without enhancing reasoning capabilities.