The study measures Google AI Overviews (AIOs), noting they activate for 13.7% of queries overall and 64.7% of question-form queries, with lower rates for politically sensitive topics. AIOs cite more credible domains than typical search results, yet 30% of cited pages are not shown in the first-page results, indicating a distinct source selection process. Among 98,020 atomic claims, 11.0% lack support from cited pages, mainly due to omissions, and over half of cited pages contain display ads, reducing publisher revenue while Google’s ads remain.
By Haofei Xu, Umar Iqbal, Jacob M. Montgomery
arXiv:2603. 08924v2 Announce Type: replace-cross Abstract: AI-powered answer engines are inherently non-deterministic: identical queries submitted at different times can produce different responses and cite different sources.
By Ronald Sielinski
arXiv:2605.16428v4 Announce Type: replace-cross
Abstract: Search engines traditionally complement online content platforms by directing users seeking information to external websites. The emergence o...
By Peibo Zhang, Ruomeng Cui, Dennis J. Zhang
arXiv:2609.00319v1 Announce Type: cross
Abstract: Online health information seeking is shifting from keyword search, where users consider a ranked list of links, to conversational systems that compos...
By Phuong Anh Nguyen, Jill Noorily, Matthew Flathers, Haruka Notsu, Laura Ospina-Pinillos, Tommy Nguyen, Samantha Clark, Aoife Keane, Grace Thompson, John Torous
arXiv:2510. 11560v2 Announce Type: replace-cross Abstract: The advent of LLMs has given rise to generative search, a new search paradigm in which LLMs retrieve information from the web related to a query and synthesize it into a single, coherent response.
By Elisabeth Kirsten, Jost Grosse Perdekamp, Qinyuan Wu, Mihir Upadhyay, Krishna P. Gummadi, Muhammad Bilal Zafar
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
The paper "Query Implied Generative Engine Optimization" introduces QI‑GEO, a method that infers user intent directly from documents to enhance visibility in Generative Search Engines. By approximating a document’s intent space, QI‑GEO identifies missing yet relevant content, improving objective scores by up to 15.9% and subjective scores by up to 17.6% on GEO‑Bench datasets. The approach yields nearly twice as many citation gains as losses, demonstrating that document‑derived intent approximations can boost content visibility without explicit query inputs.
By Shilpa Ramakrishna, William B. Andreopoulos
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 examines how large language model (LLM)-generated texts affect term-based retrieval models such as BM25. It finds that LLM outputs have smoother high-frequency and steeper low-frequency Zipf slopes, higher term specificity, and greater document-level diversity, traits that align with LLMs’ focus on reader experience. The study shows that term-based models prioritize documents whose term distributions match the queries, rather than showing an inherent bias toward the source of the text.
By Wei Huang, Keping Bi, Yinqiong Cai, Wei Chen, Jiafeng Guo, Xueqi Cheng
The paper investigates why large language models (LLMs) sometimes give outdated answers even when recent evidence is available through web search. By creating temporal facts that changed after the models’ training cutoffs, the authors evaluate a proprietary model with deployed search and six open-weight models with controlled retrieval. They find that models often rely on search snippets without opening source pages, leading to more outdated answers and unfaithful citations, and that even when retrieved evidence influences internal predictions, the final answer may still favor outdated information.
By Owen Walters, Lihu Chen, Francesca Toni
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.
By Deepa Tilwani, Yash Saxena, Seyedali Mohammadi, Ankur Padia, Edward Raff, Amit Sheth, Srinivasan Parthasarathy, Manas Gaur
arXiv:2608. 11390v1 Announce Type: new Abstract: Generative engines are reshaping the web ecosystem by making citations a key mechanism for allocating attention, attribution, and downstream value.
By Chen Xu, Zitian Guo, Chenyan Xiong