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Agentic RAG evaluation budget allocation studied

By PulseAugur Editorial · [2 sources] ·

A new research paper explores the optimal allocation of evaluation budgets in agentic retrieval-augmented generation (RAG) systems. The study, which uses HotpotQA and MuSiQue datasets, analyzes how to best distribute resources across questions, search trajectories, and repeated reads to maximize precision and efficiency. Findings suggest that broader question coverage can significantly reduce standard error compared to increasing the number of reads or trajectories, especially within certain token and cost constraints. AI

IMPACT Provides insights into optimizing the efficiency and cost-effectiveness of agentic RAG systems.

RANK_REASON The cluster contains an academic paper detailing a new evaluation methodology for agentic RAG systems.

Read on Hugging Face Daily Papers →

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

Agentic RAG evaluation budget allocation studied

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The cluster contains an academic paper detailing a new evaluation methodology for agentic RAG systems.
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COVERAGE [2]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Yibo Kong ·

    Agentic RAG Evaluation: Budget Allocation Across Questions, Trajectories, and Reads

    Evaluation budgets in agentic retrieval-augmented generation span questions, search trajectories, and repeated answers. We measure allocation precision, reading efficiency, and cost boundaries using a retrieval-feedback comparison on HotpotQA and MuSiQue. At 34.14--34.39M model t…

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

    Agentic RAG Evaluation: Budget Allocation Across Questions, Trajectories, and Reads

    Evaluation budgets in agentic retrieval-augmented generation span questions, search trajectories, and repeated answers. We measure allocation precision, reading efficiency, and cost boundaries using a retrieval-feedback comparison on HotpotQA and MuSiQue. At 34.14--34.39M model t…

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