arXiv AI
Attribution in Scientific Literature: New Benchmark and Methods
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How Do LLMs Cite? A Mechanistic Interpretation of Attribution in Retrieval-Augmented Generation
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arXiv AI
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Authority, Truth, and Citation Bias: A Large-Scale Multi-Domain Benchmark for Studying Epistemic Susceptibility in Large Language Models
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Attributable by Construction: Claim-Anchored Provenance for Multi-Document Summarization
The paper introduces CAMS, a Claim‑Anchored Multi‑Document Summarization framework that decomposes source documents into atomic claims, resolves provenance deterministically from verbatim quotes to token spans, clusters equivalent claims across documents, and rewrites summaries so each sentence ends with claim identifiers linking back to source spans. CAMS separates provenance (an invariant for each emitted sentence) from faithfulness (an objective encouraged by selection, rewriting, and verification). Evaluations on MultiNews, DiverseSumm, and zero‑shot WCEP show that CAMS matches strong baselines in summary quality while improving faithfulness and citation precision, raising attribution accuracy from 38% to 64% and reducing human verification time per claim by 3.4×.
arXiv AI
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On the Role of Citations in Preference Data
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arXiv Computation and Language
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Citing Less Critically: LLMs Reshape the Rhetoric and Reach of Scientific Citation
arXiv:2609.01432v1 Announce Type: cross Abstract: Scientific citations carry rhetorical intent. Scholars may cite prior work positively (supporting), negatively (contrasting), or neutrally (mentionin...