Hugging Face Trending Papers

Does the Citation Matter? Quality, Grounding, and Physician-Rubric Accuracy of Google's AI Overviews for Medical Information

arXiv AI
Oct 5

Measuring Google AI Overviews: Activation, Source Quality, Claim Fidelity, and Publisher Impact

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 Computation and Language
Sep 2

Sources of Truth: A Multi-Platform, Multilingual Audit of Citations in AI Mental Health Information Queries

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 Computation and Language
Oct 7

Kurate: Scalable Scientific Quality Analysis

arXiv:2610.07306v1 Announce Type: new Abstract: Scientific search systems can find papers that are relevant to a question, but they generally do not assess the quality of the evidence that those pape...

By Matthew J. Vowels, Jamie Cummins
arXiv AI
Jun 2

Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution

arXiv:2603. 05308v3 Announce Type: replace-cross Abstract: Assessing whether an article supports an assertion is essential for hallucination detection and claim verification.

By Qiao Jin, Yin Fang, Lauren He, Yifan Yang, Guangzhi Xiong, Zhizheng Wang, Nicholas Wan, Joey Chan, Donald C. Comeau, Robert Leaman, Charalampos S. Floudas, Aidong Zhang, Michael F. Chiang, Yifan Peng, Zhiyong Lu
arXiv Computation and Language
Oct 23

IndoNutriQA: An Indonesian Clinical Nutrition Question Answering Benchmark

IndoNutriQA is a newly released Indonesian clinical nutrition question‑answer benchmark comprising 911 expert‑curated question‑answer pairs drawn from real clinical‑nutrition reports. Each pair is grounded in a clinically relevant context and enriched with structured metadata such as anthropometric assessment, medical diagnosis, and document provenance. The dataset supports generative, extractive, and retrieval‑augmented QA, and includes fine‑tuned baselines and zero‑shot evaluations of contemporary large language models.

By Tirana Noor Fatyanosa, Ummu Ditya Erliana, Mahendra Data, Muhammad Nurwegiono, Eleazar Tadeo Eman, Alham Fikri Aji