Researchers are analyzing and optimizing Retrieval Augmented Generation (RAG) systems by studying the impact of context quantity, quality, and model architecture on document-based question answering. Various advanced retrieval techniques and RAG pipelines are being explored, including GraphRAG with Neo4j and an auto-merging retriever. Knowledge graphs are being utilized to enhance RAG accuracy, with Graph RAG emerging as a powerful addition. The effectiveness of long context Large Language Models (LLMs) in retrieving multiple relevant documents and providing answers across extensive input tokens is being tested. RAG is highlighted for bridging the gap between LLMs and external knowledge in the field of artificial intelligence.
Let’s explore why RAG is important and how it bridges the gap between LLMs and external knowledge. https://t.co/0Zn75qKgou #DataScience #AI #ArtificialIntelligence
Is RAG Really Dead? Testing the retrieval limits of long context LLMs RAG systems often retrieve multiple docs relevant to a user input and reason over them to return an answer. How well can long context LLMs do this across hundreds or thousands of pages of input tokens?… https://t.co/WrymZ6CZdF
⚡ Using knowledge graphs to boost RAG accuracy ⚡ Check out this guide to constructing and retrieving information from knowledge graphs in RAG applications with @neo4j and LangChain. ➡ https://t.co/MNlPCCqzn7 Graph RAG is gaining momentum as a powerful addition to…
We have dozens of advanced retrieval techniques and hundreds of notebooks showing you how to build different RAG pipelines in @llama_index. @mendableai's RAG arena now lets you try some of our most exciting retrievers in a UI: - GraphRAG with @neo4j - Auto-merging retriever -… https://t.co/E8hO65Arhg
RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems Analyzes and optimizes RAG systems by studying the impact of context quantity, quality, and model architecture on document-based question answering. 📝https://t.co/N656QjTlqN 👨🏽💻https://t.co/aHpH9JaQQw https://t.co/Xz1A9XGE8y