What are the latest and best RAG systems, mid-July 2024? IF you build your next RAG system to integrate external data (from databases), what RAG system to choose for the best performance: GraphRAG or SpeculativeRAG? My AI research channel is here to provide answers.
This video introduces a novel framework called Speculative Retrieval-Augmented Generation (Speculative RAG), designed to optimize retrieval-augmented generation systems by efficiently generating accurate responses. This method innovatively separates the retrieval-augmented generation process into two distinct phases: drafting and verification. In the drafting phase, a specialized, smaller language model (LM) generates multiple answer drafts in parallel, each from a distinct subset of retrieved documents. This approach ensures diversity in perspectives and reduces redundancy. In the verification phase, a larger, generalist LM evaluates these drafts and selects the most accurate response based on a scoring system that assesses the drafts against their rationales.
The Speculative RAG model significantly improves the efficiency and accuracy of response generation in knowledge-intensive tasks by leveraging parallel processing and optimized document sampling. The framework clusters documents based on content similarity before drafting to minimize information overload and enhance focus. The model has been tested across various benchmarks such as TriviaQA, MuSiQue, PubHealth, and ARC-Challenge, demonstrating substantial improvements in both speed and accuracy compared to conventional RAG systems.
GraphRAG is a recent development from Microsoft that significantly enhances the performance of large language models (LLMs) through the integration of knowledge graphs with Retrieval Augmented Generation (RAG). It was designed to address the shortcomings of traditional RAG, which typically relies on vector similarity for information retrieval, often resulting in inaccuracies when dealing with complex or comprehensive queries.
all rights w/ authors:
Speculative RAG: Enhancing Retrieval Augmented
Generation through Drafting
arxiv.org/pdf/...
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