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New Q2D-Web benchmark evaluates retrieval for agentic RAG systems

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced Q2D-Web, a new large-scale benchmark designed to evaluate retrieval systems within agentic retrieval-augmented generation (RAG) pipelines. This benchmark addresses limitations in existing datasets by pairing a substantial 190 million-document web corpus with 70,000 agent-reformulated search queries derived from real user conversations. Q2D-Web offers multiple relevance judgment sets and explores subcorpus sampling techniques to facilitate faster evaluation while maintaining model ranking accuracy. AI

IMPACT Provides a new standard for evaluating retrieval components in complex RAG systems, potentially improving agentic AI performance.

RANK_REASON The cluster describes a new academic benchmark for evaluating AI retrieval systems.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New Q2D-Web benchmark evaluates retrieval for agentic RAG systems

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COVERAGE [2]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Denis Bykov ·

    Q2D-Web: A Large-Scale Benchmark for Retrieval in Agentic RAG Systems

    Evaluating first-stage retrievers in large-scale production RAG requires a benchmark that pairs a large-scale corpus with a large set of agent-reformulated search queries based on real user queries and their conversation threads, and that labels many relevant documents per query.…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Q2D-Web: A Large-Scale Benchmark for Retrieval in Agentic RAG Systems

    Evaluating first-stage retrievers in large-scale production RAG requires a benchmark that pairs a large-scale corpus with a large set of agent-reformulated search queries based on real user queries and their conversation threads, and that labels many relevant documents per query.…

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