🤖 AI Summary
This work addresses the issue of factual inconsistency between generated answers and cited sources in retrieval-augmented generation (RAG) systems by proposing a corrective RAG pipeline that integrates pre-generation passage filtering with post-generation strict entailment verification. Building upon Corrective RAG and CiteFix mechanisms, the approach further incorporates an LLM-as-judge diagnostic method to enhance citation fidelity and factual grounding. The proposed framework effectively improves the faithfulness of citations while preserving answer relevance and fluency, thereby demonstrating the feasibility of strengthening citation integrity in RAG outputs. Moreover, the study advocates for a new evaluation paradigm that prioritizes strict answer traceability to source evidence, emphasizing the need for more rigorous assessment of attribution accuracy in generative retrieval systems.
📝 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 integrity as applied to RAG QA systems. We evaluate a corrective pipeline using Corrective RAG (CRAG) and CiteFix against baseline and frontier model benchmark RAG QA scores. While frontier models maximized answer relevance and fluency scores, our RAGAs LLM-as-judge diagnostics indicate that frontier models would correctly identify relevant documents without using their context in answer generation. Conversely, by filtering chunks pre-generation and enforcing strict entailment of generated claims to the cited material post-generation, our corrective pipeline marginally improved citation faithfulness and answer grounding. We propose that evaluation of trustworthy RAG QA requires metrics that reward strict answer grounding.