arXiv AI

Testing the Black Box: Structural Barriers to Independent Evaluation of Consumer-Facing Health LLMs

arXiv:2606. 08483v1 Announce Type: new Abstract: Background: Consumer-facing large language models are now a common source of health information, and they interpret and personalize responses rather than retrieve them.

arXiv AI

Jun 2

Food Noise & False Safety: A Systematic Evaluation of How LLMs Fail to Adapt to Eating Disorder Queries with Clinician Feedback

arXiv:2606. 02444v1 Announce Type: new Abstract: Recent evidence shows that people with eating disorders (EDs) are increasingly seeking guidance, advice, and emotional support from Large Language Model (LLM)-based chat systems.

By Giulia Pucci, Emily Hemendinger, Ruizhe Li, Gavin Abercrombie, Tanvi Dinkar, Arabella Sinclair

llms safety

arXiv AI

Jul 9

SycoEval-EM: Sycophancy Evaluation of Large Language Models in Simulated Clinical Encounters for Emergency Care

arXiv:2601. 16529v4 Announce Type: replace Abstract: Large language models (LLMs) deployed in clinical decision support may acquiesce to patient requests for care that conflicts with evidence-based guidelines.

By Dongshen Peng, Yi Wang, Austin Schoeffler, Sun-ha Hong, Brian Suffoletto, David Kim, Carl Preiksaitis, Christian Rose

llms agents benchmarks safety

arXiv AI

Jun 17

Towards Understanding and Measuring COGNITIVE ATROPHY in LLM Behaviour

arXiv:2606. 18129v1 Announce Type: cross Abstract: Recent incidents involving LLMs used for mental-health support reveal a critical evaluation gap: surface-level safety scores do not capture how models behave across realistic, emotionally sensitive interactions over time.

By Abeer Badawi, Moyosoreoluwa Olatosi, Negin Baghbanzadeh, Laleh Seyyed-Kalantari, Frank Rudzicz, R. Shayna Rosenbaum, Sara Pishdadian, Elham Dolatabadi

llms benchmarks safety

arXiv AI

Jul 31

When Synthetic Users Fail: A Cross-Domain Benchmark of LLM-Simulated Human Survey Responses

arXiv:2607. 26348v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as synthetic users, stand-ins for human respondents whose simulated answers feed product, policy, and market decisions.

By Zihan Chen, Di Zhu, Lei Nico Zheng

llms benchmarks

arXiv AI

Jul 8

CCBENCH: Assessing LLM Cultural Competence via Implicitly Signaled Norms using Health Queries

arXiv:2607. 05405v1 Announce Type: cross Abstract: To interact with users fairly and without stereotyping, AI models must display cultural competency, i.

By Vasudha Varadarajan, Akhila Yerukola, Mona T. Diab, Maarten Sap

llms benchmarks

arXiv AI

Aug 11

How sensitive do we want AI to be? Socio-communicative competencies of large language models in healthcare

arXiv:2608. 07511v1 Announce Type: cross Abstract: Background.

By Dorothee Amelung, Andrew M. Bean, Sabine C. Herpertz, Felix H. Krones, Guy Parsons, Adam Mahdi, Isabella Schneider

llms

arXiv AI

Jun 6

Evaluating the Utility of Personal Health Records in Personalized Health AI

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

llms safety

arXiv AI

Jul 31

The Reliability of LLMs for Medical Diagnosis: An Examination of Consistency, Manipulation, and Contextual Awareness

arXiv:2503. 10647v2 Announce Type: replace-cross Abstract: This study evaluated the diagnostic reliability of two Large Language Models (LLMs), Google Gemini 2.

By Krishna Subedi

llms rag robotics

arXiv AI

Jul 29

Retrieval-Augmented Generation in LLMs for Mental Health: Quantifying the Incremental Contribution of Retrieval Within a Layered Safety Architecture

arXiv:2607. 24817v1 Announce Type: cross Abstract: Digital mental health interventions (DMHIs) offer scalable support, but ensuring they accurately detect users' intent during volatile situations can be challenging.

By Anand Gupta, Akshat Surolia, Shubham Mishra, Shakil Imtiaz, Chaitali Sinha

llms rag safety

arXiv AI

Jun 8

When Large Language Models Fail in Healthcare: Evaluating Sensitivity to Prompt Variations

arXiv:2606. 07237v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in healthcare for tasks such as clinical question answering, diagnosis support, and report summarization.

By Mahdi Alkaeed

llms nlp benchmarks safety

arXiv AI

Jul 15

Evaluating Health Misinformation in Low-Resource Languages: Integrating Small Language Models with a Culturally-Sensitive Responsible NLP Framework (Bangla as a Case Study)

arXiv:2607. 12336v1 Announce Type: cross Abstract: Artificial Intelligence (AI) technologies, while serving as a foundational enabler for modern social media and digital health services, exert a bivalent effect by simultaneously acting as a combatant against and a spread vector for misinformation.

By Farnaz Farid, Raihan Alam, Al Al-Areqi, Farhad Ahamed, Muhammad Hassan Khan, Sadia Hossain, Irena Veljanova, Anika Tabassum Binte Hossain

llms nlp fine-tuning

arXiv AI

Jun 18

Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, User Factors, and System Prompts

arXiv:2606. 18258v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit a wide range of human-like behaviors, from expressing thoughts and emotions, to engaging in relationship-building with users, to refusing requests and maintaining boundaries.

By Sunnie S. Y. Kim, Margit Bowler, Leon A Gatys

llms

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