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Correctness is not Faithfulness in RAG Attributions
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Retrieving relevant context is a common approach to reduce hallucinations and enhance answer reliability. Explicitly citing source documents allows users to verify generated responses and increases trust. Prior work largely evaluates citation correctness - whether cited documents support the corresponding statements. But citation correctness alone is insufficient. To establish trust in attributed answers, we must examine both citation correctness and citation faithfulness. In this work, we first disentangle the notions of citation correctness and faithfulness, which have been applied inconsistently in previous studies. Faithfulness ensures that the model's reliance on cited documents is genuine, reflecting actual reference use rather than superficial alignment with prior beliefs, which we call post-rationalization. We design an experiment that reveals the prevalent issue of post-rationalization, which undermines reliable attribution and may result in misplaced trust. Our findings suggest that current attributed answers often lack citation faithfulness (up to 57 percent of the citations), highlighting the need to evaluate correctness and faithfulness for trustworthy attribution in language models.
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Cited by 3 Pith papers
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LEDGERMIND: Provenance-Constrained Multimodal Agentic Reasoning with a Structured Evidence Ledger
A provenance-constrained ledger runtime improves multimodal agent accuracy and trajectory faithfulness by binding claims to tool evidence and restricting repair to typed, non-amplifying operators.
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Evaluating and Guarding Citation Faithfulness in Agentic Scientific Synthesis
Citation-faithfulness metrics for AI science agents are verifier-dependent (3–18% on identical outputs), and a split-conformal guard provides a finite-sample catch-rate guarantee anchored on human gold.
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Tracing Facts or just Copies? A critical investigation of the Competitions of Mechanisms in Large Language Models
Attention heads in GPT-2 and Pythia-6.9B that promote factual output act by general copy suppression rather than selective counterfactual suppression, with domain-dependent effects that sharpen in larger models.
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