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@llama_index

A key milestone in building advanced RAG is being able to answer multi-hop queries; building RAG should be way more than simple question answering! MultiHop-RAG is an awesome work by Tang et al. that provides the first dataset for multi-hop queries to benchmark your advanced RAG techniques, with four types of questions: 1️⃣ Inference queries: “Which report discusses the supply chain risk of Apple, the 2019 annual report or the 2020 annual report?” 2️⃣ Comparison query: Did Netflix or Google report higher revenue for the year 2023? 3️⃣ Temporal query: Did Apple introduce the AirTag tracking device before or after the launch of the 5th generation iPad Pro? 4️⃣ Null query: "What are the sales of company ABCD as reported in its 2022 and 2023 annual reports?” Uses @llama_index embeddings/LLMs/rerankers to benchmark the performance of a simple top-k RAG setup. We’re excited to take this for a spin and try out our advanced RAG techniques (multi-document agentic reasoning, auto-retrieval, and more). Repo: https://t.co/QQKmCSV8rd Paper: https://t.co/IgTe5ZCZSx

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