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New RAG QA pipeline improves citation integrity over frontier models

By PulseAugur Editorial · [2 sources] ·

This paper details DS@GT ARC's participation in the CLEF 2026 LongEval Task 4, focusing on Retrieval-Augmented Generation (RAG) systems. The research highlights a discrepancy between standard natural language evaluation metrics and the crucial aspect of citation integrity in RAG QA. By employing a corrective pipeline with Corrective RAG (CRAG) and CiteFix, the study found that while frontier models excel in answer relevance and fluency, they do not always strictly adhere to cited sources. The proposed pipeline enforces strict entailment of generated claims to cited material, marginally improving citation faithfulness and answer grounding, suggesting a need for evaluation metrics that prioritize strict answer grounding for trustworthy RAG QA. AI

IMPACT This research suggests a need for improved evaluation metrics in RAG systems to ensure factual grounding and citation integrity, potentially influencing future development of trustworthy AI.

RANK_REASON The cluster contains a research paper detailing a new method for evaluating and improving RAG QA systems.

Read on arXiv cs.CL →

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

New RAG QA pipeline improves citation integrity over frontier models

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The cluster contains a research paper detailing a new method for evaluating and improving RAG QA systems.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Brandon Michaels, Brendon Johnson ·

    DS@GT ARC at LongEval: Citation Integrity and Factual Grounding in Scientific QA

    arXiv:2607.14400v1 Announce Type: new Abstract: This paper describes DS@GT ARC's submission to the CLEF 2026 LongEval Task 4 on Retrieval-Augmented Generation (RAG). In this submission, we examine a divergence between traditional natural language evaluation metrics and citation i…

  2. arXiv cs.CL TIER_1 English(EN) · Brendon Johnson ·

    DS@GT ARC at LongEval: Citation Integrity and Factual Grounding in Scientific QA

    This paper describes DS@GT ARC's submission to the CLEF 2026 LongEval Task 4 on Retrieval-Augmented Generation (RAG). In this submission, we examine a divergence between traditional natural language evaluation metrics and citation integrity as applied to RAG QA systems. We evalua…

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