arXiv AI
The Attribution-Compression Frontier in Retrieval-Augmented Generation
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arXiv AI
Sep 15
ATTRICITE: Training an Open 4B Model for Citation Recovery toward Faithful Attribution
arXiv:2609.14248v1 Announce Type: cross Abstract: Faithful citation attribution begins with identifying the intended source for a scientific claim. We study this source-identification capability thro...
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.
arXiv AI
Sep 17
Abstention vs. Hallucination: Benchmarking LLM Source Attribution for Scientific Citations
The paper introduces REASONS, a benchmark comprising 12,723 sentence-level citation instances across 12 arXiv subject categories, to evaluate scientific citation attribution by large language models. It proposes a dual-metric framework—Abstention Rate (AR) and Hallucination Rate (HR)—to assess the trade-off between reliability and responsiveness. Experiments on proprietary and open-source LLMs under various prompting and retrieval settings show that advanced Retrieval-Augmented Generation (RAG) reduces hallucinations but may increase abstention, while retrieval-augmented variants often maintain near-zero abstention. Human evaluation reveals a high ratio of factual hallucinations to acceptable paraphrases, underscoring the need for systems that can appropriately abstain under uncertainty.
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.
arXiv Computation and Language
Sep 18
Are Finer Citations Always Better? Rethinking Granularity for Attributed Generation
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."
arXiv Computation and Language
Sep 15
CITECHOICE: A Causal Audit of How Document Presentation Redistributes Citation Credit in Agentic Search
CITECHOICE is a causal audit that examines how the presentation of documents in an agentic search engine redistributes citation credit. Using 129 everyday‑query transcripts, the study compares structured versus prose renderings of the same source while keeping all other transcript elements fixed. The results show that structured rendering increases the target’s citation count by about half a citation per answer without adding total citations or diminishing competitors’ credit, while also revealing that rank position has a larger effect on citation rates than presentation order alone.