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Agentic RAG models evaluated for factual accuracy across datasets

By PulseAugur Editorial · [1 sources] ·

A new paper explores conformal factuality in agentic retrieval-augmented generation (RAG) systems, where models adaptively issue follow-up retrieval queries. The study evaluated six configurations using GPT-6 Luna and Llama-3.1:8b models on three datasets (HotpotQA, Natural Questions, TriviaQA). Results showed that higher target levels for factuality led to increased support rates but reduced answer availability, with full support among non-empty outputs ranging from 89.14% to 95.56% at a 95% target level. AI

IMPACT This research highlights the trade-off between factual accuracy and answer availability in agentic RAG, informing future development of more reliable AI systems.

RANK_REASON The cluster contains a research paper detailing evaluations of agentic RAG systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Agentic RAG models evaluated for factual accuracy across datasets

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The cluster contains a research paper detailing evaluations of agentic RAG systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

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

    Adaptive Retrieval, Selective Answers: Evaluating Conformal Factuality in Agentic RAG

    Retrieval-augmented generation (RAG) can improve factual grounding without guaranteeing that every generated claim is supported. Previous work has studied claim-level conformal factuality in single-stage RAG and, more recently, multi-hop RAG. We examine its behavior in agentic RA…

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