AtomCite is an agentic framework that verifies and corrects page‑level citations in multi‑page documents by parsing answers into claims, checking each claim against the cited page image, and applying a deterministic repair policy. The authors introduce DocCite, the first benchmark for this task, built on MP‑DocVQA and DUDE, containing 928 injected instances and 1,909 verified natural errors. Across Gemini, Claude, and GPT models, AtomCite achieves about 93% verification accuracy and improves citation precision from 34% to 87‑90%, while also enhancing hallucination detection in open‑source models.
By Chen Qian, Yimeng Wang, Yu Chen, Lingfei Wu, Andreas Stathopoulos
arXiv:2606. 28358v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) aims to enhance the trustworthiness of Large Language Models (LLMs) by grounding their outputs in external documents, often using inline citations for verifiability.
By Ian van Dort (University of Amsterdam), Maria Heuss (University of Amsterdam)
The paper investigates how the granularity of citations—sentence, paragraph, or document level—affects the performance of large language models in attributed generation tasks. Across models ranging from 8B to 120B parameters, enforcing fine‑grained, sentence‑level citations consistently reduces performance, with median losses of 40% and up to 338% on specific tasks, while overall answer correctness remains largely unchanged. The study finds that attribution quality peaks at intermediate, paragraph‑level granularity, suggesting that overly fine citations break semantic dependencies and overly coarse ones add noise, and that the optimal granularity depends on model scale and the amount of evidence required.
whyItMatters":"The findings reveal that the conventional preference for fine‑grained citations can actually harm model performance, indicating that attribution standards should be tailored to the model’s semantic scope rather than fixed by convention."
By Hexuan Wang, Jingyu Zhang, Benjamin Van Durme, Daniel Khashabi
arXiv:2606. 26449v1 Announce Type: cross Abstract: Retrieval-augmented systems routinely present citations alongside generated answers, yet a citation does not confirm that the corresponding source meaningfully shaped the output.
By Mohammad Faizan, Dalal Alharthi
arXiv:2609.10293v1 Announce Type: new
Abstract: In high-stakes domains such as legal practice, a language-model answer is only useful to the extent that a reader can verify each claim against the sou...
By Chen Qian, Yimeng Wang, Yu Chen, Lingfei Wu, Andreas Stathopoulos
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×.
By Shuo Guan