arXiv Machine Learning
Jul 1

Teaching LLMs to Recommend and Defer in Underrepresented Epilepsy Care

arXiv:2606. 31036v1 Announce Type: new Abstract: Specialist epilepsy expertise is scarce in resource-constrained settings, making LLM-based decision support attractive for frontline clinicians managing longitudinal treatment.

By Shreyas Rajesh, Kartik Sharma, Tonmoy Monsoor, Mehmet Yigit Turali, Richard Idro, Juliana Kayaga, Robert Sebunya, Tracy Tushabe Namata, Jessica Nichole Pasqua, Vwani Roychowdhury, Rajarshi Mazumder
arXiv AI
Oct 1

Sense and Sensitivity: Benchmarking LLM Clinical Triage Recommendations with Physician Experts

The paper introduces a benchmark of over 6,000 clinical triage scenarios, 7,000 physician annotations, and 225,000 large language model (LLM) responses to assess how LLMs perform under realistic variations in clinical text. The study finds that LLMs tend to recommend unnecessary care more often than physicians, especially when the input text is perturbed, and that LLM recommendations are more sensitive to gender and tone changes than human recommendations. These findings underscore the importance of deployment‑oriented evaluations that reflect expert physician behavior.

By Abinitha Gourabathina, Haoran Zhang, Yuexing Hao, Walter Gerych, Marzyeh Ghassemi
arXiv Computation and Language
Sep 2

PersuaRL: Reinforcement Learning-Driven Multi-Expert Selection for Persuasive Dialogue Generation in Insurance

arXiv:2609.01188v1 Announce Type: new Abstract: Large Language Models (LLMs) are revolutionizing digital communication by powering conversational agents deployed across domains such as customer servi...

By Rohan Kirti, Akash Ghosh, Aryan Vats, Niladri Ghosh, Shipra Shriparn, Roshni Ramnani, Anutosh Maitra, Sriparna Saha