arXiv AI
Sep 10

Attribution in Scientific Literature: New Benchmark and Methods

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
arXiv Computation and Language
Oct 9

CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence

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
arXiv Computation and Language
Oct 23

CiteAnchor: Source-Aware Grounding for Hallucinated Citation Detection and Resolution

The paper introduces CiteResolve, a benchmark of 423 real-world hallucinated citations annotated for source recoverability, and CiteAnchor, a source-aware grounding framework that adapts retrieval and field-level verification to each source type. CiteAnchor determines citation validity and whether a unique source can be recovered, achieving 0.841 F1 on an existing detection benchmark and 74.7% end-to-end success on CiteResolve, outperforming baselines by significant margins. The authors provide code and data at their GitHub repository.

By Chenyi Tong, Ping Nie, Yu Zhang