WildSEEK is a new dataset of 3,000 real user information‑seeking queries, manually annotated for risk‑sensitive domains and whether the query is factoid or analytical. The accompanying evaluation framework tests LLM responses against four failure criteria—sycophantic behavior, overreliance, a default US‑centric perspective, and poor handling of vulnerable populations—finding higher failure rates for analytical queries. The authors also train classifiers on WildSEEK to analyze over 1.8 million realistic queries, revealing that more than a third are high‑risk and often analytical.
By Tanise Ceron, Joachim Baumann, Elisa Bassignana, Berat Cabuk, Dirk Hovy, Debora Nozza
arXiv:2605. 18937v2 Announce Type: replace Abstract: Patient-managed Personal Health Records (PHRs) promises to empower patients to better understand their health; but information in the record is complex, potentially hindering insights.
By Rory Sayres, Kejia Chen, Ayush Jain, Matthew Thompson, Jonathan Richina, Xiang Yin, Jimmy Hu, Fan Zhang, Bob Lou, Mike Sanchez, Ines Mezerreg, Meredith Schreier, Hamsa Subramaniam, I-Ching Lee, Yugang Jia, Daniel Mcduff, Yossi Matias, Avinatan Hassidim, Dale Webster, Yun Liu, Jackie Barr, Quang Duong
arXiv:2602.06268v2 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems are increasingly integrated into clinical workflows. However, p...
By Junhyeok Lee, Han Jang, Kyu Sung Choi
arXiv:2608. 09080v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance in medical question answering and clinical reasoning tasks.
By Maryam Tahermazandarani, Adnan Mahmood, Fahmida Islam, Quan Z. Sheng
The paper evaluates how modern large language models use internal web search to answer factual questions. Using 783 static queries and 288 dynamic queries, the authors find that enabling retrieval improves accuracy on static questions but hurts confidence calibration. On dynamic queries, models often retrieve but still achieve less than 70% accuracy, mainly due to poor query formulation and source selection, indicating that internal web search works better as a quick verification tool than a full information‑retrieval system.
By Sahil Kale
arXiv:2607. 20453v1 Announce Type: cross Abstract: Large language models show promise for clinical prediction, but zero-shot performance on specialized tasks is limited by incomplete domain knowledge, especially for smaller locally deployable models.
By Jessica Sena, Shesadree Priyadarshani, Miguel Contreras, Bharat Gandhi, Scott Siegel, Subhash Nerella, Parisa Rashidi
arXiv:2511. 19829v3 Announce Type: replace Abstract: Prompt optimization has become a central mechanism for eliciting strong performance from LLMs, and recent work has made substantial progress by proposing diverse prompt evaluation metrics and optimization strategies.
By Ke Chen, Yifeng Wang, Hassan Almosapeeh, Haohan Wang
arXiv:2608. 06202v1 Announce Type: cross Abstract: Large language model (LLM) benchmark evaluations are routinely used to support claims about model safety, reliability, and deployment readiness.
By Ro Encarnaci\'on, Tina Behzad, Emma Lurie, Dana\'e Metaxa
The paper investigates the "score granularity gap" in black-box large language model (LLM) classifiers, asking how finely a confidence score can be thresholded for deployment. By comparing seven confidence construction methods across 25 model-dataset pairs, the authors find that single-shot verbalized confidence, when properly converted to a probability, ranks well but offers only a few distinct threshold values, limiting operational flexibility. The study also shows that multi-query aggregation can improve weak models but may harm strong ones, and provides concrete guidance for deployment trade-offs.
By Ao Sun, Tian Sun, Jiaxing Geng
arXiv:2501. 14940v4 Announce Type: replace-cross Abstract: Aligning large language models (LLMs) with human values is essential for their safe deployment and widespread adoption.
By Guangzhi Sun, Xiao Zhan, Shutong Feng, Philip C. Woodland, Jose Such
arXiv:2409. 07314v3 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) achieve superhuman performance on standardized medical licensing exams, these static benchmarks have become saturated and increasingly disconnected from the functional requirements of clinical workflows.
By Praveenkumar Kanithi, Cl\'ement Christophe, Marco AF Pimentel, Tathagata Raha, Prateek Munjal, Nada Saadi, Hamza A Javed, Svetlana Maslenkova, Nasir Hayat, Ronnie Rajan, Shadab Khan
arXiv:2606. 00467v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for zero-shot annotation and LLM-as-a-judge tasks, yet their reliability hinges on how model-internalized priors interact with user-provided instructions.
By Etienne Casanova, Rafal Kocielnik, R. Michael Alvarez