We're introducing Q2D-Web (Query2Doc-Web), a benchmark and public leaderboard for evaluating retrieval in agentic RAG systems. Q2D-Web tests how embedding models perform on large-scale web search using agent-reformulated search queries. Read more: ↧ Q2D-Web: Evaluating First-Stage Retrievers at Scale A benchmark and leaderboard with 70 thousand agent queries, 190 million web documents, and three sets of relevance judgements.
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We're introducing Q2D-Web (Query2Doc-Web), a benchmark and public leaderboard for evaluating retrieval in agentic RAG systems. Q2D-Web tests how embedding models perform on large-scale web search using agent-reformulated search queries. Read more:
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