arXiv:2606. 23989v1 Announce Type: cross Abstract: End-to-end large language models (LLMs) produce fluent multi-document summaries but remain prone to hallucination, and the attributions they offer are typically coarse (whole documents or passages) and generated post hoc, leaving each summary statement hard to verify.
By Shuo Guan
CiteVQA is a benchmark that evaluates multimodal large language models on both answering document questions and providing element‑level bounding‑box citations for the evidence used. It contains 1,897 questions across 711 PDFs in seven domains and two languages, with ground‑truth citations generated via an automated pipeline and strict quality control. The evaluation metric, Strict Attributed Accuracy (SAA), rewards a prediction only when both the answer and the cited region are correct, revealing widespread attribution hallucination in current models.
By Dongsheng Ma, Jiayu Li, Zhengren Wang, Yijie Wang, Jiahao Kong, Weijun Zeng, Jutao Xiao, Jie Yang, Bangrui Xu, Yuhan Wang, Bin Wang, Conghui He
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:2607. 23804v1 Announce Type: cross Abstract: Context attribution methods for large language models (LLMs) identify which input context contributes to the model response.
By Quoc-Huy Trinh, Lin Zhu, Sebastian Szyller
OARelatedWork is a large‑scale dataset for generating entire related‑work sections from open‑access papers, comprising 94,450 papers across multiple domains. It includes full texts of cited works and is designed so every source paper is available in full, enabling controlled evaluation of full‑text related‑work generation. The paper benchmarks various models, shows that large language models struggle with massive full‑text contexts, and introduces a statement‑level evaluation framework to better assess long‑form structured outputs.
By Martin Docekal, Martin Fajcik, Pavel Smrz
The paper introduces REASONS, a benchmark of 12,723 sentence-level citation instances across 12 arXiv subject categories, to evaluate scientific citation attribution under different evidence conditions. It proposes a dual-metric framework—Abstention Rate (AR) and Hallucination Rate (HR)—to balance reliability and responsiveness. Experiments with proprietary and open-source LLMs across various prompting and retrieval settings show that advanced Retrieval-Augmented Generation (RAG) reduces hallucinations but increases abstention, while adversarial metadata can push hallucination rates above 85%. Human evaluation confirms a high ratio of factual hallucinations to acceptable paraphrases, underscoring the need for systems that can appropriately abstain under uncertainty.
By Deepa Tilwani, Yash Saxena, Seyedali Mohammadi, Ankur Padia, Edward Raff, Amit Sheth, Srinivasan Parthasarathy, Manas Gaur